Data Science in Golf Strokes Gained vs Traditional Metrics - Model Building.ipynb

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    "tags": []
   },
   "source": [
    "# **Data Science in Golf: Strokes Gained vs Traditional Metrics - Model Building**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a7db5c-d9de-4aa1-9f87-b415005c5a0c",
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   "source": [
    "--------------------------------\n",
    "### **Context** \n",
    "-------------------------------\n",
    "Golf is a great example of how data science can provide valuable insight. Golf is a sport steeped in tradition and has been traditionally evaluated using metrics like scoring average, fairways hit, and greens in regulation. However, these traditional metrics have limitations and may not provide a complete picture of a player's skill. Data science and analytics, specifically the use of Strokes Gained, can help to overcome these limitations by providing a more accurate and comprehensive assessment of a player's performance. \n",
    "\n",
    "The use of a normalized set of metrics allows for better comparison across different courses and fields, and can help identify areas for improvement. \n",
    "\n",
    "Additionally, there are misconceptions about the game of golf on what skills result in shooting lower scores and winning tournaments, but by using data science and Strokes Gained, it is possible to dispel those misconceptions and focus on facts and facts-based analysis, which can lead to better performance and ultimately better results. \n",
    "\n",
    "#### **Strokes Gained**\n",
    "\n",
    "Strokes Gained (often called the Moneyball of golf) is a relatively new metric in golf that aims to measure a player's performance relative to the rest of the field. It does this by comparing a player's performance on a given hole or round to the expected performance of an \"average\" golfer. \n",
    "\n",
    "Traditional golf metrics, such as scoring average, fairways hit, and greens in regulation, do not take into account the difficulty of the course or the level of competition. Additionally many of these metrics were popularized because they sound good, but often they provide limited insight or in some cases act as misleading metrics. In the example of fairways hit in regulation, this metric does not take into account distance left to the hole. In these cases a shot 10 yards from the hole in the rough would be ranked lower than a shot 300 yards+ from the hole in the fairway, which is clearly counter to what it should be. \n",
    "\n",
    "Strokes Gained, on the other hand, uses data from thousands of rounds to calculate the expected performance on each hole and then compares a player's actual performance to that expectation. This allows for a more accurate assessment of a player's skill and can help identify areas where they need to improve.\n",
    "\n",
    "----------------------------\n",
    "### **Objective**\n",
    "-----------------------------\n",
    "The objective of this project is to analyze data from the 2010 to 2018 PGA tour season using various metrics, including Strokes Gained, to gain insight into the performance of professional golfers. The specific objectives of this project are as follows:\n",
    "\n",
    "* Draw insight from data from the 2010 to 2018 PGA tour season.\n",
    "    * Which features lead to the greatest success in golf? Specifically compare how strokes gained stats compare to the traditional stats. \n",
    "     * Investigate if the traditional view that short game (putting and chipping) is more valuable than long game skills (driving and approach shots)?\n",
    "    * Investigate if the traditional view that driving accuracy (fairways hit %) is more valuable than driving distance?\n",
    "* Determine a the best predictive feature to measure \"sucess\" in golf,\n",
    "* Build a prediction model for \"success\" in golf based on based on that feature.\n",
    "* Compare the effectiveness of the Strokes Gained metrics to traditional metrics in predicting success by conducting feature analysis on the impact of different metrics. \n",
    "\n",
    "Overall, the goal of this project is to use data science to gain a better understanding of golf performance and to identify areas for improvement. By analyzing data from the PGA tour, this project will provide insights that can be used to improve the performance of golfers at all levels.\n",
    "\n",
    "--------------------------\n",
    "### **Data Description**\n",
    "--------------------------\n",
    "Dataset contains PGA Tour Data from 2010 to 2018 seasons (special note data is pre-covid season 2020). \n",
    "\n",
    "**Name the target variable - Looks like wins and top 10 might be out for the small sample size. Fedex cup might be out as well because of the fedex cup weekend? Money might be out as it looks like there might be a lot of nulls for some reason?**\n",
    "\n",
    "* `Player Name` - Name of the player\n",
    "* `Rounds` - Number of rounds they played during that specific season\n",
    "* `Fairway Percentage` - Percentage of Fairways hit off the tee. \n",
    "* `Year` - Season played. Because multiple seasons where recored there will be duplicates in column `Player Names` that represent the different seasons they recorded these stats.  \n",
    "* `Avg Distance`- Average **driving** distance\n",
    "* `gir`- Greens hit in regulation\n",
    "* `Average Putts`- Average puts in a round (18 holes)\n",
    "* `Average Scrambling`- Measure of a golfers performance when they miss the green in regulation. Value is a percentage of how likely they are able to save par in that instance. \n",
    "* `Average Score`- Average number of strokes taken per round, that is a average of all a players scores that season. The number is adjusted by the stroke average (difficulty) of each tournament.\n",
    "* `Points`- Points on the PGA Tour website refer to a player's standing in the FedEx Cup points race, which is a season-long competition that awards points to players based on their performance in each tournament throughout the season. \n",
    "    * Certain tournaments on the PGA Tour award more FedEx Cup points than others. The FedEx Cup points system is designed to award more points to players who compete in tournaments with stronger fields\n",
    "    * Additionally, tournaments that are part of the FedEx Cup playoffs (The Northern Trust, BMW Championship, Tour Championship) also award more FedEx Cup points than regular season events. The point distribution increases as the playoffs progress and the field gets smaller.\n",
    "* `Wins` - Total number of wins that season\n",
    "* `Top 10`- Total number of top 10 placements that season\n",
    "* `Money`- Total money earned from tournament winnings. \n",
    "\n",
    "**Strokes Gained Discription**\n",
    "\n",
    "Strokes Gained metrics are calculated by comparing the number of strokes a player takes to the number of strokes an average player would take on the same shot. \n",
    "\n",
    "Example: In the case where a player has a 16-ft 11-inches putt, which has an average strokes gained of 1.826 (on average players take 1.826 strokes to get it in the hole). If the player putts it to 4ft, which has a average strokes gained of 1.12 and then they then proceeded to putt it in. The calculation is:\n",
    "\n",
    "* 1.826(SG 1rst putt) -1.12(SG 2nd putt) - 1(putt in) = -0.29 \n",
    "\n",
    "That player receives a negative 0.29 strokes gained for those putts. That means they performed 0.29 strokes worse than the average player. Compared to if they holed the putt out in 1 shot.\n",
    "\n",
    "* 1.826(SG 1rst putt) - 1(putt in) = 0.826\n",
    "\n",
    "The player would receive a postive 0.826 for that putt. That means they performed 0.826 almost an entire stroke better than the average player. A similar calculation can be done for ever shot on the course.  \n",
    "\n",
    "* `Average SG Putts`- The average strokes gained for all putts hit in a round (calculated as shown above) that is then subtracted by the field average strokes gained to account for the tournament difficulty. The sum of each round is then divided by the number of rounds played. \n",
    "* `Average SG Total`- The average strokes gained performed compared to the rest of the field at each tournament. \n",
    "* `SG:OTT`- Strokes Gained Off-The-Tee measures a player's performance off the tee (from the tee box) compared to the rest of the field in a given tournament or round. \n",
    "* `SG:APR`- Strokes Gained Approach measures a player's performance on approach shots (shots from the fairway or rough, played to the green) compared to the rest of the field in a given tournament or round. \n",
    "* `SG:ARG`- Strokes Gained Around-The-Green measures a player's performance on shots played around the green (such as chip shots, pitches, and bunker shots) compared to the rest of the field in a given tournament or round."
   ]
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   "source": [
    "-------------------------------\n",
    "### **Recommendations From EDA** \n",
    "-------------------------------\n",
    "* Average_Score is a good metric that focuses on playing well. It is adjusted for the average score of each tournament, but that should only increase its validity. It is our best option of all predictive features. \n",
    "* Dropped a number of irrelevant or potentially biased columns from our analysis, we should repeat this step for the model building phase. Droped Columns include:\n",
    "    * `Points`, `Wins`, `Top_10`, and `Money` - potential predictive features that would not perform as well as `Average_Score`.\n",
    "    * `Player_Name`, `Year` - irrelevant features.\n",
    "    * `Rounds` - irrelevant to performance stats, but could biase the data as better players tend to play fewer tournaments. \n",
    "    * Kept `Average_SG_Total` for analysis but should drop from model analysis as it too tightly correlated with `Average_Score`. While it would prove valuable as a predictive feature, it would prevent us from seeing which component features of strokes gained are more critical. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e22c1c7-8a3f-4989-99b5-afdcda97caa3",
   "metadata": {},
   "source": [
    "## **Importing Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6eaf60-c784-4947-97f5-44cc5dd6d24b",
   "metadata": {},
   "source": [
    "### **Import the necessary libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9db90160-a552-4a50-ae08-873e1d204687",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score,precision_score,recall_score,f1_score\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeRegressor #Used for a decision tree with a continuouse dependent variable\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor #Used for a random forest with a continuouse dependent variable\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ad138f-c78b-40cc-830f-84d6bac2ca94",
   "metadata": {},
   "source": [
    "### **Read the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f3df8d5-e4e6-4fa1-adc3-1d0f9f8d596f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hm=pd.read_csv(\"pgaTourData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41a5b28d-c4a4-4be4-bc46-8053e0fa64cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=hm.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631b5970-b091-4275-8f67-ef3132c05268",
   "metadata": {},
   "source": [
    "#### Formate the column names to have _ instead of spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a7dbb1e-9cf7-48ef-b049-699a8dde33d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Player_Name', 'Rounds', 'Fairway_Percentage', 'Year', 'Avg_Distance',\n",
      "       'gir', 'Average_Putts', 'Average_Scrambling', 'Average_Score', 'Points',\n",
      "       'Wins', 'Top_10', 'Average_SG_Putts', 'Average_SG_Total', 'SG:OTT',\n",
      "       'SG:APR', 'SG:ARG', 'Money'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# replace spaces with underscores in column names\n",
    "for col in data.columns:\n",
    "    data.rename(columns={col: col.replace(\" \", \"_\")}, inplace=True)\n",
    "\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8c004d-9d49-4e74-8757-b0920ba1ebf7",
   "metadata": {},
   "source": [
    "#### **Data Overview**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce4ad456-348a-4929-bca9-78c6c32af04a",
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Rounds</th>\n",
       "      <th>Fairway_Percentage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Avg_Distance</th>\n",
       "      <th>gir</th>\n",
       "      <th>Average_Putts</th>\n",
       "      <th>Average_Scrambling</th>\n",
       "      <th>Average_Score</th>\n",
       "      <th>Points</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Top_10</th>\n",
       "      <th>Average_SG_Putts</th>\n",
       "      <th>Average_SG_Total</th>\n",
       "      <th>SG:OTT</th>\n",
       "      <th>SG:APR</th>\n",
       "      <th>SG:ARG</th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Henrik Stenson</td>\n",
       "      <td>60.0</td>\n",
       "      <td>75.19</td>\n",
       "      <td>2018</td>\n",
       "      <td>291.5</td>\n",
       "      <td>73.51</td>\n",
       "      <td>29.93</td>\n",
       "      <td>60.67</td>\n",
       "      <td>69.617</td>\n",
       "      <td>868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.207</td>\n",
       "      <td>1.153</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.960</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>$2,680,487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ryan Armour</td>\n",
       "      <td>109.0</td>\n",
       "      <td>73.58</td>\n",
       "      <td>2018</td>\n",
       "      <td>283.5</td>\n",
       "      <td>68.22</td>\n",
       "      <td>29.31</td>\n",
       "      <td>60.13</td>\n",
       "      <td>70.758</td>\n",
       "      <td>1,006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.337</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.194</td>\n",
       "      <td>$2,485,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chez Reavie</td>\n",
       "      <td>93.0</td>\n",
       "      <td>72.24</td>\n",
       "      <td>2018</td>\n",
       "      <td>286.5</td>\n",
       "      <td>68.67</td>\n",
       "      <td>29.12</td>\n",
       "      <td>62.27</td>\n",
       "      <td>70.432</td>\n",
       "      <td>1,020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.437</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>$2,700,018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ryan Moore</td>\n",
       "      <td>78.0</td>\n",
       "      <td>71.94</td>\n",
       "      <td>2018</td>\n",
       "      <td>289.2</td>\n",
       "      <td>68.80</td>\n",
       "      <td>29.17</td>\n",
       "      <td>64.16</td>\n",
       "      <td>70.015</td>\n",
       "      <td>795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.273</td>\n",
       "      <td>$1,986,608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brian Stuard</td>\n",
       "      <td>103.0</td>\n",
       "      <td>71.44</td>\n",
       "      <td>2018</td>\n",
       "      <td>278.9</td>\n",
       "      <td>67.12</td>\n",
       "      <td>29.11</td>\n",
       "      <td>59.23</td>\n",
       "      <td>71.038</td>\n",
       "      <td>421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.026</td>\n",
       "      <td>$1,089,763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Player_Name  Rounds  Fairway_Percentage  Year  Avg_Distance    gir  \\\n",
       "0  Henrik Stenson    60.0               75.19  2018         291.5  73.51   \n",
       "1     Ryan Armour   109.0               73.58  2018         283.5  68.22   \n",
       "2     Chez Reavie    93.0               72.24  2018         286.5  68.67   \n",
       "3      Ryan Moore    78.0               71.94  2018         289.2  68.80   \n",
       "4    Brian Stuard   103.0               71.44  2018         278.9  67.12   \n",
       "\n",
       "   Average_Putts  Average_Scrambling  Average_Score Points  Wins  Top_10  \\\n",
       "0          29.93               60.67         69.617    868   NaN     5.0   \n",
       "1          29.31               60.13         70.758  1,006   1.0     3.0   \n",
       "2          29.12               62.27         70.432  1,020   NaN     3.0   \n",
       "3          29.17               64.16         70.015    795   NaN     5.0   \n",
       "4          29.11               59.23         71.038    421   NaN     3.0   \n",
       "\n",
       "   Average_SG_Putts  Average_SG_Total  SG:OTT  SG:APR  SG:ARG       Money  \n",
       "0            -0.207             1.153   0.427   0.960  -0.027  $2,680,487  \n",
       "1            -0.058             0.337  -0.012   0.213   0.194  $2,485,203  \n",
       "2             0.192             0.674   0.183   0.437  -0.137  $2,700,018  \n",
       "3            -0.271             0.941   0.406   0.532   0.273  $1,986,608  \n",
       "4             0.164             0.062  -0.227   0.099   0.026  $1,089,763  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4767e12c-758f-438b-b829-fb55091eb941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2312, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab6353d7-8feb-426c-a4e5-d4cfc2bf71d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2312 entries, 0 to 2311\n",
      "Data columns (total 18 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Player_Name         2312 non-null   object \n",
      " 1   Rounds              1678 non-null   float64\n",
      " 2   Fairway_Percentage  1678 non-null   float64\n",
      " 3   Year                2312 non-null   int64  \n",
      " 4   Avg_Distance        1678 non-null   float64\n",
      " 5   gir                 1678 non-null   float64\n",
      " 6   Average_Putts       1678 non-null   float64\n",
      " 7   Average_Scrambling  1678 non-null   float64\n",
      " 8   Average_Score       1678 non-null   float64\n",
      " 9   Points              2296 non-null   object \n",
      " 10  Wins                293 non-null    float64\n",
      " 11  Top_10              1458 non-null   float64\n",
      " 12  Average_SG_Putts    1678 non-null   float64\n",
      " 13  Average_SG_Total    1678 non-null   float64\n",
      " 14  SG:OTT              1678 non-null   float64\n",
      " 15  SG:APR              1678 non-null   float64\n",
      " 16  SG:ARG              1678 non-null   float64\n",
      " 17  Money               2300 non-null   object \n",
      "dtypes: float64(14), int64(1), object(3)\n",
      "memory usage: 325.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f82f3f-83b8-4645-b613-b67cf1b56afb",
   "metadata": {},
   "source": [
    "All features are numerical except for `Player_Name`,`Points` and`Money`. \n",
    "* `Points` and`Money` **should be converted into numerical features.\n",
    "\n",
    "Multiple columns contain NULL values, all features exept: Player_Names and Year. Looks like there are 1678 values that are consitently non-null?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8c0b51-586a-4fe4-b06a-ab31d75ff7b6",
   "metadata": {},
   "source": [
    "#### **Drop Null Values**\n",
    "**Attempting to drop NULL rows with the above hypothesis that they are all from the same group**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a095706c-8029-4e06-ae40-d7605616ce92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy to check assumption\n",
    "data_clean = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "014bb9f2-6506-4ed2-a7b7-17838a694557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove rows with null values in 'Average_SG_Putts' column\n",
    "data_clean = data_clean.dropna(subset=['Average_SG_Putts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad1b85bb-4645-485f-8fce-7398b3f0d454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1678 entries, 0 to 1677\n",
      "Data columns (total 18 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Player_Name         1678 non-null   object \n",
      " 1   Rounds              1678 non-null   float64\n",
      " 2   Fairway_Percentage  1678 non-null   float64\n",
      " 3   Year                1678 non-null   int64  \n",
      " 4   Avg_Distance        1678 non-null   float64\n",
      " 5   gir                 1678 non-null   float64\n",
      " 6   Average_Putts       1678 non-null   float64\n",
      " 7   Average_Scrambling  1678 non-null   float64\n",
      " 8   Average_Score       1678 non-null   float64\n",
      " 9   Points              1674 non-null   object \n",
      " 10  Wins                283 non-null    float64\n",
      " 11  Top_10              1353 non-null   float64\n",
      " 12  Average_SG_Putts    1678 non-null   float64\n",
      " 13  Average_SG_Total    1678 non-null   float64\n",
      " 14  SG:OTT              1678 non-null   float64\n",
      " 15  SG:APR              1678 non-null   float64\n",
      " 16  SG:ARG              1678 non-null   float64\n",
      " 17  Money               1674 non-null   object \n",
      "dtypes: float64(14), int64(1), object(3)\n",
      "memory usage: 249.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f031bda-7c3f-4dc4-85b0-f57128d38474",
   "metadata": {},
   "source": [
    "**Hypothesis was correct** there are 1678 values out of the original 2312 that are not null on a majority of the features. Will use this cleaned data as our dataset moving forward. \n",
    "* `Wins` and `Top_10` still contain a majority null, it is likely these are players who did not score on these metrics and can likely be changed to 0.\n",
    "* `Money` has 4 NULL values, we can examine these individually. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b203f86-10de-4845-95d4-a5afd09ba672",
   "metadata": {},
   "source": [
    "### **Choosing our predictive feature**\n",
    "The goal of our analysis is to determine which features best predict success in golf, but we have many choices for which feature we can use. \n",
    "* We can use `Average_Score`, `Points`, `Wins`, `Top_10`, and `Money`.\n",
    "* While `Wins` and `Top_10` are great measures of success, so few players achieve them that they would not function as a good predictive feature.\n",
    "* `Points` seems like a good metric on the surface, but fedex points are not distributed evenly in golf. Some tournaments are weighted more and a small selection of top players are allowed to play in the fedex cup which gives the most points. This all to say perhaps those different weights are balanced, but if we have a better option it would be prudent to use it isntead. \n",
    "* `Money` - Is a good overall weight of success on the pga tour, but there are likely mutliple confounding factors involved. \n",
    "* `Average_Score` is a great metric that focuses on playing well. It is adjusted for the average score of each tournament, but that should only increase its validity. \n",
    "\n",
    "**The best choice for predicitve feature is `Average_Score` the other predictive features will be dropped**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def2c0e0-3120-410b-9f03-c22c5aac765c",
   "metadata": {},
   "source": [
    "#### **Dropping Collumns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c10cd948-6557-4751-aa85-dd486153f0d8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# drop 'Points', 'Wins', 'Top_10', 'Money' columns\n",
    "data_clean.drop(columns=['Points', 'Wins', 'Top_10', 'Money'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6e4cf4c-2ca2-4c86-acf6-cf57b2a14d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean.drop(columns=['Average_SG_Total'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9874d0-104c-4f1e-854b-87634814cf84",
   "metadata": {},
   "source": [
    "* Dropping `Player_Name`, `Year` as they have no impact.\n",
    "\n",
    "* Dropping `Rounds` as it is not a measure of how well golf is played. **It would could skew the data as better players play fewer rounds.**\n",
    "\n",
    "* Dropping `Average_SG_Total` as we want the individual SG metrics to predict Average Score and `Average_SG_Total` encomposes all those metrics and therefore is to tightly correlated to the prediction target and will dominate the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ed8a62e-eb68-412e-911c-724d54c4f6c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop 'Player_Name', 'Year' and 'Rounds'\n",
    "data_clean.drop(columns=['Player_Name', 'Year', 'Rounds'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbc6e3a4-cdc4-45ad-954b-bcc25d6f985e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1678 entries, 0 to 1677\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Fairway_Percentage  1678 non-null   float64\n",
      " 1   Avg_Distance        1678 non-null   float64\n",
      " 2   gir                 1678 non-null   float64\n",
      " 3   Average_Putts       1678 non-null   float64\n",
      " 4   Average_Scrambling  1678 non-null   float64\n",
      " 5   Average_Score       1678 non-null   float64\n",
      " 6   Average_SG_Putts    1678 non-null   float64\n",
      " 7   SG:OTT              1678 non-null   float64\n",
      " 8   SG:APR              1678 non-null   float64\n",
      " 9   SG:ARG              1678 non-null   float64\n",
      "dtypes: float64(10)\n",
      "memory usage: 144.2 KB\n"
     ]
    }
   ],
   "source": [
    "data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655898d1-aaa9-4f78-b309-a1d2a5fbb68a",
   "metadata": {},
   "source": [
    "The remaining data is are numerical therefore we no longer need to convert any features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5faec54a-9528-4628-8152-11850b9e3d33",
   "metadata": {},
   "source": [
    "#### Downloading A Excel Copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6d978e85-d764-481f-8cc8-24b0a9b129f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame is successfully written to Excel spreadsheet\n"
     ]
    }
   ],
   "source": [
    "# Specify the file name\n",
    "filename = \"data_clean.xlsx\"\n",
    "\n",
    "# Write DataFrame to an Excel spreadsheet\n",
    "data_clean.to_excel(filename, index=False)\n",
    "\n",
    "print(\"DataFrame is successfully written to Excel spreadsheet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51879689-a874-440f-bb8e-59b9b5673a34",
   "metadata": {
    "id": "7QgAXSWfuS-M",
    "tags": []
   },
   "source": [
    "## **Data Preparation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffaf537f-53bb-4f51-b5d1-fbf4c8dd6ec4",
   "metadata": {
    "id": "jSUhpu0fuS-M"
   },
   "source": [
    "### **Separating the target variable from other variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ab662897-e491-44bd-aadd-a915718e505e",
   "metadata": {
    "id": "2_IZEwKruS-N"
   },
   "outputs": [],
   "source": [
    "# Drop the dependent variable `Average_Score` from the dataframe and create the X(independent variable) matrix\n",
    "\n",
    "X = data_clean.drop(columns = 'Average_Score') #make a copy called X which is a dataframe with \"Average_Score\" removed\n",
    "\n",
    "# Create y(dependent varibale)\n",
    "Y = data_clean['Average_Score'] #Y is a series (column) containing the \"Average_Score\" label data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ca0fa2-b27c-4365-9976-a8c22efeeabd",
   "metadata": {
    "id": "La_3-i5quS-P"
   },
   "source": [
    "### **Splitting the data into 70% train and 30% test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdd6c9c9-4070-4bd2-a4c9-1e50e95c45ec",
   "metadata": {
    "id": "sgF5mdtCuS-P"
   },
   "outputs": [],
   "source": [
    "# Split the data into training and test set\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.30, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80eea46-a52d-496a-8a8a-321de7b82bec",
   "metadata": {},
   "source": [
    "## **Model Building**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33cc4cc8-492b-4475-8379-f28becfaa660",
   "metadata": {},
   "source": [
    "### **Basic Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af2788c1-cd44-4eee-a626-1871b795d1fc",
   "metadata": {
    "id": "_n6jIx9yuS-q"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit a decision tree regression model to the training data\n",
    "d_tree_1 = DecisionTreeRegressor()\n",
    "\n",
    "d_tree_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbc24f2e-6ccb-4d01-b2ad-ddddb4f5e12e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  1.0\n",
      "Test Accuracy:  0.6651200125114656\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training data\n",
    "train_accuracy = d_tree_1.score(x_train, y_train)\n",
    "\n",
    "print(\"Train Accuracy: \", train_accuracy)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "test_accuracy = d_tree_1.score(x_test, y_test)\n",
    "\n",
    "print(\"Test Accuracy: \", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62146221-6122-4a91-9c8f-fb94471ca7d2",
   "metadata": {},
   "source": [
    "Model is very **overfitted**. Let's try hyperparameter tuning using **GridSearchCV to find the optimal max_depth to reduce overfitting** of the model. We can tune some other hyperparameters as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4969ee60-9d04-482b-ac78-a3c0fc2c2c44",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Model Results Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0921e650-2b51-4c4d-aba8-8b0c1073a70d",
   "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>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model_Name, Training_Accuracy, Testing_Accuracy]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a table \n",
    "results = pd.DataFrame(columns = ['Model_Name','Training_Accuracy','Testing_Accuracy'])\n",
    "\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "913d35dd-af3c-443b-9f01-c4101d17ea45",
   "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>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d_tree_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.66512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model_Name Training_Accuracy Testing_Accuracy\n",
       "0   d_tree_1               1.0          0.66512"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.append({'Model_Name':'d_tree_1',\n",
    "                          'Training_Accuracy':1.0,\n",
    "                          'Testing_Accuracy':0.6651200125114656,},\n",
    "                            ignore_index=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79633851-3cfc-4ecd-8d23-302bc0e81fc9",
   "metadata": {},
   "source": [
    "### **Tuned Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aef8248b-3460-4100-8f2c-544ee502b8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters:  {'max_depth': 8, 'min_samples_leaf': 4, 'min_samples_split': 4}\n"
     ]
    }
   ],
   "source": [
    "# Define the hyperparameter grid to search\n",
    "param_grid = {'max_depth': [3, 4, 5, 6, 7, 8],\n",
    "              'min_samples_split': [2, 3, 4, 5], #minimum number of samples required to split an internal node\n",
    "              'min_samples_leaf': [1, 2, 3, 4]} #minimum number of samples to be a leaf node\n",
    "\n",
    "# Fit a decision tree regression model to the training data using GridSearchCV\n",
    "d_tree_2 = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5)\n",
    "d_tree_2.fit(x_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters: \", d_tree_2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd38c0a9-7d18-4cc0-8fd5-d475088e910f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.898391519589668\n",
      "Test Accuracy:  0.6825467642802832\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training data\n",
    "train_accuracy = d_tree_2.score(x_train, y_train)\n",
    "\n",
    "print(\"Training Accuracy: \", train_accuracy)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "test_accuracy = d_tree_2.score(x_test, y_test)\n",
    "\n",
    "print(\"Test Accuracy: \", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b876977-6c29-48a0-bec1-e532eb8f3704",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Model Results Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b00f9d83-6e4b-4f64-b121-abd3957e37ca",
   "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>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d_tree_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.66512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d_tree_2</td>\n",
       "      <td>0.898392</td>\n",
       "      <td>0.682547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model_Name Training_Accuracy Testing_Accuracy\n",
       "0   d_tree_1               1.0          0.66512\n",
       "1   d_tree_2          0.898392         0.682547"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.append({'Model_Name':'d_tree_2',\n",
    "                          'Training_Accuracy':0.898391519589668,\n",
    "                          'Testing_Accuracy':0.6825467642802832,},\n",
    "                            ignore_index=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4b118b-7728-4b1c-9d59-95584a5e4300",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* Tuned Decision Tree is has reduced overall overfitting\n",
    "* The model is still performing ~20% better on the training data and only scoring an overall accuracy of 68%\n",
    "\n",
    "Will perform visualization on the tree to gain insight, but will try an **random forest** model to see if it performs better. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b783dca-5037-4209-a87e-327252b29837",
   "metadata": {},
   "source": [
    "#### **Visualize The Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bbdb4b05-71fd-4221-90d3-9dc524024f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the best decision tree model found by GridSearchCV\n",
    "d_tree_best = d_tree_2.best_estimator_\n",
    "d_tree_best.fit(x_train, y_train)\n",
    "\n",
    "# Plot the decision tree with a maximum depth of 4\n",
    "plt.figure(figsize=(20,10))\n",
    "tree.plot_tree(d_tree_best, filled=True, max_depth=4, feature_names=X.columns, fontsize=10, precision=2,\n",
    "               class_names=['Negative', 'Positive'], node_ids=True, impurity=False, label='all')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a1d682-84c8-4fde-8ef6-4b44ea458c02",
   "metadata": {},
   "source": [
    "#### **Feature Importance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ddf40f54-018d-4659-a9f1-67aa0e770c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HjmHcuHFixSQiIiKSJY0gCIo7LvgyDnfq9RWAYaK+hThWAykpd6VOUaqnp0uC41QONYwR4DiVRA1jBEr3OCU73ElEREREL44ljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGRC1pkZGR6NKlCzp16oSgoKDHHo+Ojkb37t3RrVs3jBw5EhkZGQCAsLAwtGzZEt27d0f37t0RGBgoZkwiIiIi2bEU6xMbDAYEBgYiNDQU1tbW6N+/P5o3b466desCALKysjB79mxs2bIF9vb2WLJkCZYtW4YZM2bg7NmzmDp1Ktzd3cWKR0RERCRros2kxcTEwNnZGZUqVYKNjQ1cXV0RFRVlejw/Px8+Pj6wt7cHADg4OODGjRsAgDNnziAsLAweHh6YOHGiaYaNiIiISC1EK2kpKSnQ6XSmbb1eD4PBYNquXLkyOnbsCADIzc3F6tWr0aFDBwCATqfDyJEjERERgWrVqsHX11esmERERESyJNrhTqPRCI1GY9oWBKHI9kOZmZkYNWoU6tevj549ewIAli9fbnp8yJAhpjJXXHZ2ti+YWh10uvJSRwAgnxxi4ziVQw1jBDhOJVHDGAHljlO0kla1alUcO3bMtJ2amgq9Xl/kOSkpKfjss8/g7OyMadOmAXhQ2rZs2YLBgwcDeFDutFptid47LS0LRqPw3wagYKmpmVJHgE5XXhY5xMZxKocaxghwnEqihjECpXucFhaaZ04siXa408XFBbGxsbh9+zZycnKwa9cutGrVyvR4YWEhRowYgc6dO2P69OmmWTYbGxusXbsWp06dAgBs2LChxDNpRERERKWdaDNp9vb28Pb2hpeXF/Lz89GnTx84Ojpi6NChGDt2LG7evInz58+jsLAQO3fuBAA0bNgQfn5+WLx4MWbPno3c3FzUqlUL/v7+YsUkIiIikiWNIAiKOy74Mg536vUVgGGivoU4VgMpKXelTlGqp6dLguNUDjWMEeA4lUQNYwRK9zglO9xJRERERC+OJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhopd0m7evIkDBw6gsLAQycnJYmYiIiIiUr1ilbT9+/ejf//+mDNnDtLS0tC1a1dER0eLnY2IiIhItYpV0pYvX45NmzahQoUK0Ov12LhxI5YuXSp2NiIiIiLVKlZJKywsLHJz9Lffftt0r00iIiIiMr9ilbRy5cohOTnZVMyOHTuGMmXKiBqMiIiISM2KdYP1CRMm4NNPP0Vqaio++ugjXL58GcuWLRM7GxEREZFqFaukNW3aFJs2bcLJkydhNBrRuHFjVK5cWexsRERERKpVrMOdCQkJmDhxIlq3bo3XXnsNgwcPxl9//SV2NiIiIiLVKlZJmz17Nvr27QsAcHBwwJgxY+Dj4yNqMCIiIiI1K1ZJy8nJQceOHU3bHTp0QFZWlmihiIiIiNSuWCVNo9EgISHBtJ2UlAQLC95RioiIiEgsxbpw4IsvvsDAgQNRr149AMBff/2FgIAAUYMRERERqVmxSlrbtm0RFRWFEydOQKvV4t1334WdnZ3Y2YiIiIhUq9jHLJOSkgAABQUFOH78OHbt2iVaKCIiIiK1K9ZM2owZM3Dw4EHUrFnTtE+j0aBTp06iBSMiIiJSs2KVtNjYWGzfvh22trZi5yEZaeBYF6k3U6SOUWK6qnqcO50odQwiIqL/pFglrVq1aixoKpR6MwUYJnWKkktdXfqKJRER0b8V+7ZQ3t7eaNu2LcqWLWvaz8OdREREROIoVkk7efIkAODXX3817eM5aURERETiKVZJ+/nnn8XOQURERESPKFZJu3z5MjZs2IDs7GwIggCj0Yh//vkHwcHBz3xdZGQkVq5ciYKCAgwaNAgDBgwo8nh0dDSWLVsGQRDw+uuvY968eahYsSKSk5MxadIkpKWloXbt2ggICMArr7zy4qMkIiIiKmWKtU7ahAkTkJ+fj5MnT6J69epITEw03X3gaQwGAwIDA7Fx40aEh4cjJCQEiYn/u+IuKysLs2fPxurVqxEREQEHBwcsW7YMADBnzhx4enoiKioKDRs2xIoVK/7DEImIiIhKn2KVtHv37mHOnDlo2bIlWrVqhXXr1uHPP/985mtiYmLg7OyMSpUqwcbGBq6uroiKijI9np+fDx8fH9jb2wMAHBwccOPGDeTn5+Po0aNwdXUFAPTq1avI64iIiIjUoFiHOytVqgQAqFmzJi5dugRHR0doNJpnviYlJQU6nc60rdfrcfr0adN25cqV0bFjRwBAbm4uVq9ejYEDB+LOnTuwtbWFpeWDaDqdDgaDoUSDsrPjciHPotOVlzqC6OQ0RjllEZMaxqmGMQIcp5KoYYyAcsdZrJJWs2ZN+Pn5oWfPnpg+fTqys7NRUFDwzNcYjcYiRU4QhCcWu8zMTIwaNQr169dHz549YTAYHnve8wrhv6WlZcFoFEr0GjVJTc2UOoLo5DJGna68bLKISQ3jVMMYAY5TSdQwRqB0j9PCQvPMiaViHe6cPXs2nJyc8M4776Bv3744cuQI5s6d+8zXVK1aFampqabt1NRU6PX6Is9JSUmBp6cnHBwc4OfnBwCoUqUKMjMzUVhY+NTXERERESldsUraqlWrTOeIeXp6Yvny5di+ffszX+Pi4oLY2Fjcvn0bOTk52LVrF1q1amV6vLCwECNGjEDnzp0xffp002yZlZUVnJycTJ8/PDy8yOuIiIiI1OCZhzuXLl2Ku3fvYvv27cjKyjLtz8/Px6FDhzBjxoynvtbe3h7e3t7w8vJCfn4++vTpA0dHRwwdOhRjx47FzZs3cf78eRQWFmLnzp0AgIYNG8LPzw8+Pj6YOnUqVq5ciWrVqmHRokVmGi4RERFR6fDMkvbuu+/izJkzsLCwMF08AABarRYBAQHP/eQeHh7w8PAosm/NmjUAgEaNGiEhIeGJr6tevToX0KWXhjeSJyIiOXpmSWvdujVat24NABg9evRLCUT0svFG8kREJEfFOieN65QRERERvVzFWoKjdu3amDFjBpycnGBjY2PazxusExEREYmjWCUtPT0d6enp+Oeff0z7NBoNSxoRERGRSIpV0h6exF9QUABBEGBlZSVqKCIiIiK1K9Y5aWlpaRgyZAgaN24MR0dHeHl5lfhWTURERERUfMUqab6+vmjcuDFiYmIQExMDJycnzJ49W+RoREREROpVrJJ2+fJljB49GhUqVEDlypUxduxYXLlyRexsRERERKpVrJJWUFCA+/fvm7ZzcnJKfNNzIiIiIiq+Yl040KVLFwwePBi9evWCRqPBli1bTPfyJCIiIiLzK1ZJGzVqFKpWrYrff/8dRqMRvXr1Qp8+fcTORkRERKRaxSppANCuXTuUL18eWq0W77//Pg93EhEREYmoWOek7d69G506dcL69euxdu1adOzYEUeOHBE7GxEREZFqFWsmLTAwEBs2bICDgwMA4Ny5c5gxYwbCwsJEDUdERESkVsWaSStbtqypoAFAgwYNeLiTiIiISETFKmmtWrXC6tWrkZ2djfv37yMkJARvvfUWMjIykJ6eLnJEIiIiIvUp1uHONWvWoLCwEIsWLSqyf+vWrdBoNIiPjxclHBEREZFaFauknTt3TuwcRERERPSIYpW03Nxc7Nmz57FDmwMGDBAjExEREZHqFaukDR8+HJmZmXj99ddN+zQaDUsaERERkUiKVdJSUlKwY8cOsbMQERER0f9XrKs769Wrh9TUVLGzEBEREdH/V6yZNDc3N3Tu3Bn16tWDpeX/XrJ+/XrRghERERGpWbFK2vLlyzF8+HC88cYbYuchIiIiIhSzpJUrVw5Dhw4VOwsRERER/X/FOifNxcUFQUFBSElJQXp6uunP80RGRqJLly7o1KkTgoKCnvq8yZMnIzQ01LQdFhaGli1bonv37ujevTsCAwOLE5OIiIhIMYo1k7Zu3Trk5eVh7ty50Gg0EAThuXcaMBgMCAwMRGhoKKytrdG/f380b94cdevWLfIcHx8fxMbGwtnZ2bT/7NmzmDp1Ktzd3f/D0IiIiIhKr2eWtId3Gti4cWOJP3FMTAycnZ1RqVIlAICrqyuioqIwevRo03MiIyPRvn1703MeOnPmDC5fvoxVq1bBwcEBM2fORMWKFUucgYiIiKi0emZJGzNmDIAHC9c+6uFM2p49e5762pSUFOh0OtO2Xq/H6dOnizxnyJAhAIDjx48X2a/T6fDpp5+iadOmWLRoEXx9fbFw4cJiDIeIiIhIGZ5Z0vbu3fvCn9hoNBYpdw+LXXEsX77c9PGQIUPQsWPHEr23nZ1tiZ6vNjpdeakjiE4NYwTkNU45ZRGLGsYIcJxKooYxAsodZ7HOSXsRVatWxbFjx0zbqamp0Ov1z31dZmYmtmzZgsGDBwN4UO60Wm2J3jstLQtGo1Ci16hJamqm1BFEp4YxAvIZp05XXjZZxKKGMQIcp5KoYYxA6R6nhYXmmRNLxbq680W4uLggNjYWt2/fRk5ODnbt2oVWrVo993U2NjZYu3YtTp06BQDYsGFDiWfSiIiIiEo70WbS7O3t4e3tDS8vL+Tn56NPnz5wdHTE0KFDMXbsWDRq1OiJr9NqtVi8eDFmz56N3Nxc1KpVC/7+/mLFJCIiIpIl0UoaAHh4eMDDw6PIvjVr1jz2vPnz5xfZdnJyQlhYmJjRiIiIiGRNtMOdRERERPTiWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGWNKIiIiIZIgljYiIiEiGLKUOQEQvRwPHuki9mSJ1jBLTVdXj3OlEqWMQEb10LGlEKpF6MwUYJnWKkktdXfqKJRGROfBwJxEREZEMiVrSIiMj0aVLF3Tq1AlBQUFPfd7kyZMRGhpq2k5OTsaAAQPg5uaGzz//HPfu3RMzJhEREZHsiFbSDAYDAgMDsXHjRoSHhyMkJASJiYmPPWfEiBHYuXNnkf1z5syBp6cnoqKi0LBhQ6xYsUKsmERERESyJFpJi4mJgbOzMypVqgQbGxu4uroiKiqqyHMiIyPRvn17dO7c2bQvPz8fR48ehaurKwCgV69ej72OiIiISOlEu3AgJSUFOp3OtK3X63H69OkizxkyZAgA4Pjx46Z9d+7cga2tLSwtH0TT6XQwGAxixSQiIiKSJdFKmtFohEajMW0LglBk+2me9LzivO5Rdna2JXq+2uh05aWOIDo1jBHgOF82ueQQG8epHGoYI6DccYpW0qpWrYpjx46ZtlNTU6HX65/7uipVqiAzMxOFhYXQarXFft2j0tKyYDQKJc6sFqmpmVJHEJ0axghwnC+TTldeFjnExnEqhxrGCJTucVpYaJ45sSTaOWkuLi6IjY3F7du3kZOTg127dqFVq1bPfZ2VlRWcnJywfft2AEB4eHixXkdERESkJKKVNHt7e3h7e8PLyws9evSAu7s7HB0dMXToUJw5c+aZr/Xx8cGmTZvQpUsXHDt2DOPGjRMrJhEREZEsiXrHAQ8PD3h4eBTZt2bNmseeN3/+/CLb1atXx88//yxmNCIiIiJZ4x0HiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhiylDkBEZC4NHOsi9WaK1DFKTFdVj3OnE6WOQUQyw5JGRIqRejMFGCZ1ipJLXV36iiURiY+HO4mIiIhkiCWNiIiISIZELWmRkZHo0qULOnXqhKCgoMcej4+PR69eveDq6orp06ejoKAAABAWFoaWLVuie/fu6N69OwIDA8WMSURERCQ7op2TZjAYEBgYiNDQUFhbW6N///5o3rw56tata3rOpEmT8NVXX6Fx48aYNm0aNm3aBE9PT5w9exZTp06Fu7u7WPGIiIiIZE20mbSYmBg4OzujUqVKsLGxgaurK6KiokyPX79+Hbm5uWjcuDEAoFevXqbHz5w5g7CwMHh4eGDixInIyMgQKyYRERGRLIlW0lJSUqDT6Uzber0eBoPhqY/rdDrT4zqdDiNHjkRERASqVasGX19fsWISERERyZJohzuNRiM0Go1pWxCEItvPenz58uWm/UOGDEHHjh1L9N52drYvGlsVdLryUkcQnRrGCHCcSiKnMcopi5jUME41jBFQ7jhFK2lVq1bFsWPHTNupqanQ6/VFHk9NTTVt37p1C3q9HpmZmdiyZQsGDx4M4EF502q1JXrvtLQsGI3CfxuAgqWmZkodQXRqGCPAcSqJXMao05WXTRYxqWGcahgjULrHaWGheebEkmiHO11cXBAbG4vbt28jJycHu3btQqtWrUyPV69eHWXKlMHx48cBAFu3bkWrVq1gY2ODtWvX4tSpUwCADRs2lHgmjYiIiKi0E20mzd7eHt7e3vDy8kJ+fj769OkDR0dHDB06FGPHjkWjRo0QEBCAGTNmICsrCw0aNICXlxe0Wi0WL16M2bNnIzc3F7Vq1YK/v79YMYmIiIhkSdTbQnl4eMDDw6PIvjVr1pg+rl+/PjZv3vzY65ycnBAWFiZmNCIiIiJZ4x0HiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGSIJY2IiIhIhljSiIiIiGRI1Ht3EhGR+TVwrIvUmylSxygxXVU9zp1OlDoGUanBkkZEVMqk3kwBhkmdouRSV5e+YkkkJR7uJCIiIpIhljQiIiIiGWJJIyIiIpIhljQiIiIiGWJJIyIiIpIhljQiIiIiGeISHEREJDuldS04gOvBkfmwpBERkeyU1rXgAK4HR+bDw51EREREMsSSRkRERCRDLGlEREREMsSSRkRERCRDvHCAiIhIIqX1KlZewfpyiFrSIiMjsXLlShQUFGDQoEEYMGBAkcfj4+Mxffp03Lt3D05OTpgzZw4sLS2RnJyMSZMmIS0tDbVr10ZAQABeeeUVMaMSERG9dKX1KlZewfpyiHa402AwIDAwEBs3bkR4eDhCQkKQmFi0dU+aNAmzZs3Czp07IQgCNm3aBACYM2cOPD09ERUVhYYNG2LFihVixSQiIiKSJdFm0mJiYuDs7IxKlSoBAFxdXREVFYXRo0cDAK5fv47c3Fw0btwYANCrVy8sXboUffv2xdGjR7F8+XLT/o8//hiTJk0SKyoRERGJiId1X4xoJS0lJQU6nc60rdfrcfr06ac+rtPpYDAYcOfOHdja2sLS0rLI/pKwsND8x/TPV7NmTcBO9Lcxv5rF//tRwxgBjlP2+G/28aerYJyldoyAOsZZwn+zNmXKoeaXNUUMJJKN4naK531u0Uqa0WiERvO/NxcEocj20x7/9/MAPLb9PJUri3/+2uXLl0V/D1F8XfynqmGMAMcpe/w3+xg1jLPUjhFQxzj5b/alEO2ctKpVqyI1NdW0nZqaCr1e/9THb926Bb1ejypVqiAzMxOFhYVPfB0RERGRGohW0lxcXBAbG4vbt28jJycHu3btQqtWrUyPV69eHWXKlMHx48cBAFu3bkWrVq1gZWUFJycnbN++HQAQHh5e5HVEREREaqARBEEQ65NHRkZi1apVyM/PR58+fTB06FAMHToUY8eORaNGjZCQkIAZM2YgKysLDRo0wLx582BtbY3r169j6tSpSEtLQ7Vq1bBo0SJUrFhRrJhEREREsiNqSSMiIiKiF8PbQhERERHJEEsaERERkQyxpBERERHJEEsaERERkQyxpBERERHJEEsaERGRQmRnZyMhIQGCICA7O1vqOPQfsaRJ7N69e8jPzy+yLy8vD6tWrZIoEf0XAwcOhJeXl+nPoEGDMHz4cHzzzTfIyMiQOp7ZXLlyBRERERAEATNnzkTv3r1x5swZqWPRC0hPT0dMTAwAYNWqVRg7diyuXLkicSrz279/v9QRRBcbG4vu3btj5MiRuHXrFtq2bYtDhw5JHYv+A5Y0CQUHB6N58+Zo0aIFzp49CwCIioqCq6srIiMjJU5nPmPGjJE6wktTt25dODg4YNq0aZg2bRoaNWqE8uXLw97eHtOnT5c6ntl8+eWXMBqN2LNnDy5fvowvv/wSfn5+Uscyq/z8fKSnpwMAduzYgfDwcFMxVZIJEyYgPj4eMTExiIqKQrt27RT1b/WhBQsWSB1BdIsWLcLGjRtRoUIF6HQ6BAUFwd/fX+pYosjLy8PKlSsxefJkZGVl4dtvv0VeXp7UscxOtBus0/OtXbsWmzdvxrVr17BmzRpUqFABe/fuxZgxY9C3b1+p45nNtWvXpI7w0pw6dQqhoaGm7fr166N3794ICAhAeHi4dMHM7P79++jRowemT58ODw8PODk5KeobZFJSEoYNG4bhw4ejX79+CAgIwPvvv4+EhATcv39fUf8/MzIy8Nlnn2Hu3Lno2bMnevTogfXr10sdy+xq1KiBL7/8Eu+++y7Kli1r2t+jRw/pQpmZ0WiETqczbdetW1fCNOLy9fVFlSpVcP78eWi1Wly5cgXTpk1DQECA1NHMiiVNQuXKlUP9+vVRv359zJgxAx988AF27twJW1tbqaOZ1b1793Ds2LGnzkC89957LzmRePLz83Hp0iW89dZbAIBLly7BaDQiNzf3scPapZlWq8XOnTuxf/9+fPHFF4iOjoaFhXIm5ufNm4dp06ahffv2AIDy5ctj3rx5uHr1KiZMmKCokmY0GnH27FlER0djw4YNiI+PR2FhodSxzK5y5coAHvwi9SgllbSqVati37590Gg0uHv3LoKCgvDaa69JHUsU586dQ1hYGA4ePIhy5crhm2++gYeHh9SxzI4lTUJardb0ccWKFeHv7w8rKysJE4kjNTUVS5cufWJJ02g0ivqtfcaMGRg6dCjs7OxgNBpx9+5d+Pv7Y9myZejevbvU8czG19cXP/74I2bNmgW9Xo/ffvtNUYc7r169aipowP9+wNeoUQN3796VKpYoJk2aBH9/f3z66aeoUaMG+vXrhy+//FLqWGY3b948qSOIztfXF35+frhx4wY6duyI5s2bw9fXV+pYotBoNMjLy4NGowEA3Llzx/SxkrCkSejRf1A2NjaKLGgAULNmTUUVsWdp3rw5oqOjcfHiRVhYWKBOnTqwsrJC06ZNFfUNZP/+/UV+6AUGBmLRokVwcHCQMJV41q1bZ/pYaf9Pb968WeT/56ZNmxAUFARnZ2cJU5nP8OHDsWrVKrRr1+6J/wf37NkjQSpx2NnZYciQIVi0aBEyMzNx9uxZ6PV6qWOJwsvLC5988glSU1Ph5+eH6OhojBo1SupYZseSJqHLly/Dy8sLgiCYPn6UWoqNkly/fh0bNmxARkZGkZlDpfwWHxAQgLS0NOzduxeXL1827S8sLMSpU6cwfvx46cKZUc2aNfH777/jww8/LLL/4MGDeOONNyRKZV4//vgjsrKyEBwcjOvXr5v2FxYWIjIyEgMGDJAwnfnMnTsXwOMXMF2/fh1ly5bFxYsXUa9ePSmimV1AQADOnz+PH374ATk5OVixYgWOHTumyIu3evTogYYNGyIuLg6FhYVYuXIl6tevL3Uss2NJk5BaltkYNmyY1BFemnHjxsHJyQlOTk6Kmjl7qFOnTkhMTMSRI0fw/vvvm/ZrtVqMHDlSwmTmNW7cOHz22Wfo3bu36ZzJEydOYMuWLfjpp58kTmcetWrVMl1V/ihra2vMnz9fgkTieDiTtHfvXsTHx6NDhw4QBAH79++HXq/Hjh074OHhgcGDB0sb1Az279+PrVu3Angw7nXr1qFnz56KLGkPL8R65ZVXAAAJCQm4fPky3nzzTcWUboAlTVLvv/8+/vrrLyQlJaFBgwaKPcFzzZo16NKli9QxXoqCggJMmTJF6hiicXR0hKOjIzIzM9GzZ88ijwUFBaFWrVrSBDOzd955B7/88gt++OEHLFy4EADQqFEjBAUFoWbNmhKnM482bdqgTZs2qFChwmOz+Lt27ZIolXhSU1MRGhqKChUqAHgwszZixAiEhISgV69eiihpBQUFyM3NNRUXJV2s9G979uzB+fPn0bFjxyKlOzs7WzGlG2BJk1RQUBACAgLw5ptv4urVq5g7dy5cXV2ljmV2SltX6lmaNWuGvXv3omXLlrC2tpY6jtk9eogsMzPTtF9ph8iABzNNTzrp+tq1a3j99dclSGRe27dvR15eHn788UdTcQEe/KBftWoVOnXqJGE687tz546pvABAmTJlkJGRAUtLS8XMevfv3x+9evVCu3btADw4PK+k/5OPSk1NRVhYmKJLN8CSJqmNGzciOjoadnZ2SEhIgI+PjyJL2q1bt/Dtt98+9fHRo0e/xDTiioqKwoYNG4rs02g0iI+PlyiReanlENnff/+NZcuWoVKlSpgwYQJeeeUVZGVlYcWKFQgKCnpsGYfS6N69ezhx4gTu3buHuLg4036tVgtvb28Jk4mjU6dOGDRoEDp37gyj0Yhdu3ahffv2CA8PL7K2WGk2ePBgNGvWDEePHoWlpSUWLFiAd955R+pYolBD6QYAjaCmaQ6Z6dGjR5EFTj08PBR1p4GHWrZsif79+z/1cSWVNLVISkpCnTp1pI4hmv79+8PR0RGpqal47bXX0LJlS0yePBnVq1fH5MmT0bRpU6kjmk1sbCw++OADqWO8FPv27cPhw4eh1Wrh4uKC1q1b488//0Tt2rVRsWJFqeP9ZwUFBTh06JDpThkPKWktuIcWLlyIkydPFindzZo1Q61atbBt2zasXbtW6ohmwZImoZ49eyIsLOyp20qh1HE9KiQkBB999NFTZwyVVkSVvpyBq6srdu7ciby8PLi7u6OgoAATJkxA165dpY5mdgMHDnzi15JXl5c+X3zxBZKTk1GnTp0iX1OlXF3+b0ov3QAPd0oqPT29yEzav7eV8tuPGn4PUMMYH/Xzzz+bPi4oKMDu3bsVdVuocuXKAXhwGPf+/fv48ccfUbt2bYlTiePRK/8KCgqwZ8+eIueoUelx4cIF7NixQ1GH+57F3t4ebm5uEAQBhYWF2Lx5M/r06SN1LLPiTJqEnreqt1J++0lPT0elSpUAAHfv3oVGo0H58uWlDUVm16tXryL3LS3NHp39/fdpCWrQt29f/Prrr1LHoBIaNWoUfHx8FLuA7aNmzJiBP/74AxkZGXjzzTeRkJCApk2b4vvvv5c6mllxJk1Czyphhw4deolJxFWpUiVERERg2bJluHr1KjQaDWrUqIExY8Yo5l5r9evXf+Jvr4IgKOrCgYeOHj1q+lgQBFy6dAn379+XMJF5PZzVFgQBGRkZj5U0pcxyA0BycrLpY0EQkJiY+Ng5TVQ65Obmws3NDfXq1StydbkSD13HxMRg586dmDt3Lry8vJCTk6Ooi5ceYkmTkdu3b2PLli3YtGkT7t+/j4MHD0odySx27NiBlStXYsaMGXjvvfdQUFCAEydOYP78+bCysoKbm5vUEf+zhIQEqSO8VEuXLjV9rNFoULlyZUV9g3R2djZd8fjoxw8pqaR9/PHHpo81Gg2qVKmCGTNmSJiIXtTw4cOljvDS6PV6WFlZoU6dOrhw4QK6du1aZFkgpeDhThmIi4tDcHAwoqOjodFoMGfOHLi7uyvmHoH9+vXDwoULUaNGjSL7r1y5gvHjx2Pz5s0SJTO//Px8BAcH448//oClpSVcXFzQp08f1ZwjogaHDh1Cy5YtpY5B9ETnz59Hdna26Tyta9euKe48LeDBRRLvvPMOPvjgAyxYsAD9+/fHsmXLEBUVJXU0s+JMmoR+/PFHhISEwMrKCp07d8YXX3yBTz/99LGV3Eu7+/fvP1bQAOCNN95Q1CEyAPD19UVWVhZ69uwJo9GIrVu34sKFC4qZmbh06RJmzpyJS5cuoUmTJvD19VXsnTIepcRZboPBAH9/f9PXcsKECbxgoJR72nlaSixpfn5+OHDgABwdHdGpUyds27YNc+bMkTqW2VlIHUDNFi1ahPr162PmzJkYMWIEatWqpcgZl9zcXOTk5Dy2Pzs7G4WFhRIkEs+ff/6JwMBAtGvXDh06dMCSJUseO1RWmvn4+MDd3R3BwcFo0KCBog5xPklcXBy8vb3RunVrLFu2DCNHjlTMMiPTpk2DXq/H+PHjkZeXp5gLldQsJiYGv/32G1xdXTF37lysX78eubm5UscSRVBQkGlJnIEDB2LlypU4fPiwxKnMjzNpEjp48CAiIyPx9ddf49atW+jcubOiljF4yM3NDTNmzMDXX3+NMmXKAAAyMzMxffp0xVw48JC9vT2uXr1qmjlMSUlRzGrmAJCVlWU6h8nb21uR64YB6pjlNhgMpivhWrRooajz7NRKDedpBQQEIC0tDXv37sXly5dN+wsLC3Hq1CmMHz9eunAiYEmTUKVKlTBw4EAMHDgQCQkJ2LJlCwoKCtC1a1d4enoq5p5rY8aMwYwZM+Di4oI6deqgoKAAly9fRrdu3TBixAip45nFwwVB79y5g27duuG9996DhYUFTpw4gbfeekvqeGZjaVn0W4ZSzpv8t0WLFqF9+/bw9PSEk5MTNBqN4ma5H/3aWVlZKfZrqSb29vZYtWqV6TwtAIr7xb9Tp05ISkrCkSNH8P7775v2a7VajBw5UsJk4uCFAzKTn5+PvXv3IiwsDN99953Ucczq2rVrOHv2LDQaDRwdHVGtWjWpI5nNH3/88czHH/1mUpqp5S4Z6enpiIyMRGhoqGmWe8eOHfj999+ljmY2avlaqklWVhYOHDiArl274ueff0ZMTAwGDx6M5s2bSx3N7DIzM1Wx3iZLmgykpqY+dkjsSftKO7WM88KFC7h7926Rfe+9955Eaczr3+vBPVwHTqnrwQEwzXJv27YNVapUUcwsd8OGDWFvb2/aNhgMsLe3N30tlXLunZqsWrXqsWU4Fi1apLhDgAAQFhaG+fPnm77XKvV7EEuaDDxpRXMl/lb7pHEqbTX38ePH49y5c0VW/NZoNIpcTPJpzp07hwYNGkgdw+yUNst9/fr1Zz5evXp1Rf4SpUSPnqfVrl070/6H52nt3LlTwnTi6NChA1asWIF69epJHUVULGlEZtS5c2ds27YNWq1W6iiSUdIvGGqZ/X0aJX0tlez06dNISkrC0qVLMXbsWNN+rVYLR0dH1KpVS7pwIvH09MTGjRuljiE6XjggA/n5+bCyssKff/6J/Px8WFhYoFmzZlLHMjs1jPPdd9/FP//8gzfffFPqKJJR0u99Q4cOfWymd9iwYaopLkr6WiqZo6MjHB0d0aFDB5QpUwbW1tb4559/8Pfff+ONN96QOp4oGjRogLFjx6JFixamVQMAZd0NBGBJk5TBYMDo0aPRpUsXfPLJJ/D29sbrr7+O69evY8qUKXB1dZU6olmoZZzAg1sIubu7Q6/XQ6vVqvL8HiVdBfmkQ/FqKWiAsr6WarB+/XokJSVh4sSJGDBgAN566y0cOnRIMYtpPyorKwuvvPIK/vzzzyL7WdLIbL7++mv06NHDdBJyxYoV8fPPPyMhIQF+fn6KKS9qGSfw4MTdn376SRWr8KuBGmZ/STn27NmDjRs3Yv369ejWrRsmT56MXr16SR1LFA8XX87IyEDFihUlTiMe3nFAQgkJCU+8Sqx+/fq4efOmBInEoZZxAkDlypXh5OSE6tWrF/lDpYvBYEDfvn2xYcMGAA8W7l26dCkmTZqkyJOwSRmMRiPKli2Lffv2oXXr1jAajU+824sSJCQkwM3NDd27d4fBYEDHjh1x7tw5qWOZHWfSJGRhUbQj//rrr6aPy5Ur97LjiEYt4wSAWrVqoV+/fnBxcSmyOOjo0aMlTPVyKeE8JjXN/j6LEr6WavLBBx/A3d0dZcuWxXvvvYePP/4Ybdu2lTqWKObOnYvly5djwoQJsLe3x+zZs+Hj44PNmzdLHc2sOJMmoVdffRWnT582bT/8oX769Gm8+uqrUsUyO7WMEwBee+01tG7dWvGrt+fn5wN4cK/So0eP4vjx46bHli1bJlUss1HT7C/wYKHp/fv3o7CwEFevXjXtV+K5TEo2ZcoUrF69Gps2bYKFhQVmzpyJyZMnSx1LFDk5OahTp45pu0WLFoq7uwLAmTRJjRw5EqNGjcKoUaNMt545fvw4VqxYgcDAQKnjmY1axgk8PmMmCAKuXbsmURrze9ZFIFOnTkWnTp1M9y0tzdQ0+7t9+3asXLkSOTk5CAkJQf/+/TF58mR0794dTk5OUsejYli2bBnGjBmDL7/88omPPzx/S0kqVaqEhIQE08UtERERijw3jSVNQh988AECAwOxcuVKLFiwABqNBo0aNcKiRYvQpEkTqeOZjVrGCQAhISH45ptvipwH8vrrr2P37t0SpjKf5x0G7NSpk8QJzePh7K+joyMAZc/+rlmzBr/88gs+/vhj2NnZISwsDJ988gm6d+8udTQqpoeLRyvl9nPFMXv2bEyZMgWXLl2Ck5MTatasabpfqZKwpEksMzMTPj4+eOONN7B7925s3rwZv//+Oxo1aqSoQ2ZqGeeqVauwdetWLF68GN7e3jhw4ABOnDghdSyzSUhIwJIlSx7br7TDgGqa/bWwsICtra1pW6/XPzaTSPL28C4DHTt2xN9//42yZcvijTfeKLJ+mNK88cYbWLZsGWxsbGA0GpGWloaaNWtKHcvs+D9RQt9//z2+/fZb5OXlISEhAZMmTUKHDh2QkZEBf39/qeOZjVrGCQB2dnaoUaMGHBwccPHiRQwYMAAXLlyQOpbZqOUw4MPZ3927d+Ojjz7CRx99hB07dihy9vett97Chg0bUFBQgPj4eMycORP169eXOhaVQE5ODiZPngxnZ2eMGDECgwcPxvvvv485c+aYzh9VmvXr12Po0KGwsbFBRkYGRowYgZCQEKljmR1LmoS2bt2KDRs2oG7duti2bRvatWuHvn37YubMmTh06JDU8cxGLeMEHhSVI0eOwMHBAfv27UNqaipyc3OljmU2aroI5OHs7/HjxzFv3jxYW1vj999/V9wPvVmzZsFgMKBMmTKYNm0abG1t4ePjI3UsKoGHs9v79+/H4cOHcfjwYezZswfZ2dmK+0X4oU2bNiEoKAjAg/vMhoaGmpbMURKWNAlpNBrT7ENcXBw+/PBD034lUcs4AWDmzJnYt28fPvzwQ6Snp6Nz5874+OOPpY5lNg8PAwYHByMxMRFJSUnYtGkTxo4dizFjxkgdz2zUNPtrY2ODCRMmYMuWLQgLC8OUKVOKHP4k+Ttw4ADmzZtX5BelV199FXPnzsWRI0ckTCae/Px8WFtbm7aVdNrMo3hOmoS0Wi3u3r2L7OxsxMfHo0WLFgCA69evw9JSOV8atYwTALZt22a6wkoJS1H8278vAgEe3Ddw4cKFijoMuHXrVoSEhKBcuXIICAgwzf4KgoAuXbpIHc+sWrdujZSUFFSoUAEAcPfuXVSoUAGvv/46vvrqK7z99tsSJ6Tnsba2hlarLfZ+JejQoQMGDRqEzp07Q6PRYOfOnWjfvr3UscxOWT8hS5lhw4ahR48eKCgoQJ8+faDX67F9+3YEBgZi1KhRUsczG7WMEwD27duHcePGKXKW8CEnJyd8//33UscQ1b9nfz09PU37lea9996Dm5sbOnToAODBrExUVBQGDhyIOXPmIDg4WOKE9DzPutBDif9mAWD8+PHYvXs3jh49CktLS3h5eZn+DSsJS5qE3Nzc0KRJE9y5c8d0ou4rr7yCr776Cs2bN5c4nfmoZZzAg7V73Nzc0KBBgyJXVilpnaJ9+/ahbt26qFGjBqKjo7F582a8/fbbGDlypGIOOahp9vfSpUsICAgwbbdu3RpLlizBO++8g/v370uYjIrr8uXL8PLyemy/IAj4559/JEgkvj59+iAsLAxubm5SRxGVsr7blEL29vawt7c3bbdu3VrCNOJRyzh79uwpdQRRff/999i+fTu++eYbJCQkYOLEiZg+fTri4+Ph7++P6dOnSx3RLNQ0+1uhQgUEBwejW7duMBqNiIyMRMWKFZGUlASj0Sh1PCqGVatWSR3hpXv11Vdx7NgxODo6Fjk3TWk0Am/ORmQ2WVlZ2Lp1KwYMGACDwYDg4GAMGzZMMctTdOvWrci5WsnJyVi0aJHpXK0dO3ZIHdFsDAZDkdnfAwcOoGzZsoqb/TUYDPDz88Phw4eh1Wrh4uKCadOmYefOnahZsyZatWoldUQyg549eyIsLEzqGGbj7OyM9PR0AA8O6QqCAI1Gg/j4eGmDmRln0ojMaOLEiXBwcADw4JCu0WjE5MmTFXMRgZrO1VLL7K+9vT2WLl1aZF9ubi4GDhwoUSISg9LmY5R61eq/saQRmVFycjK+++47AICtrS28vb0VdXsdNZ2rpRZ79+7F4sWLkZ2dDUEQYDQakZOTo5ofgmqhtF+k8vLy8MMPP+Dvv//GzJkz8eOPP2LYsGGKO/TJddKIzEij0RS5w0BSUpKiysvDc7X69etX5FytwYMH47PPPpM6Hr2AefPmYdq0aahTpw4CAgLQpUsXxS0zQsrj6+uL7OxsnDt3DlqtFv/88w+mTZsmdSyzU85PDyIZmDJlCj799FPTYbI7d+4o6qa/xblSNzU1FTqdTsqYVALly5eHs7MzTpw4gczMTEyaNIkljWTv3LlzCAsLw8GDB1GuXDn4+/vDw8ND6lhmx5JGZEYuLi7Yt28fLl68CEtLS7z55puKm35/3rlaw4YNU9QJykpXtmxZ/P3336hTpw7++OMPODs7K+7WV6S8c9I0Gg3y8vJMh3Hv3LmjuEO6AA93EpnNvn37cPXqVVhbW+PmzZtYvHgxvvvuO9X9wFPaDwOl8/b2xuLFi9G2bVvExsaiRYsWilwUVO2GDRsmdQSz8vLywieffILU1FT4+fmhd+/eGDRokNSxzI5LcBCZwaPrhxUUFKB///6m9cO0Wq1i1g8rDqVd6q90wcHB6N+/v2k7IyMDFStWlDARvahHb/ElCAIyMzMVfYuvxMRExMXFobCwEO+//77pFAwl4eFOIjNQ070eSVk2bNhQpKSxoJVearjFl9FoxObNm3Hx4kU0bdoUAwYMkDqSqFjSiMxATeuHkbJUrVoVXl5eePfdd4vcymz06NESpqIXoYZbfM2ePRsJCQlo1qwZvvvuO/z111+K/rfKkkZkBlw/7H94BkXp0rhxY6kjkJmo4RZfR48exfbt26HRaHDnzh0MGjSIJY2Ink1N93p86Nq1a0hMTMSHH36I5ORk1KhRAwAwY8YMiZNRSYwePRrZ2dm4cuUK6tWrh9zcXNjY2Egdi15AQEAA/Pz8sGDBAlhaWuKDDz7AN998g507d2LChAlSxzOLMmXKmI5QVK5cWfFHK3jhAJGZPO9ej0paP2z79u1YuXIlcnJyEBISgm7dumHy5MmKuruCWsTGxmLWrFkoLCxESEgI3N3dsXDhQrRs2VLqaFRC0dHRaNOmjaJn7/99YZLSL1RiSSN6SZT0zaRnz574+eef8fHHHyM8PBwpKSn45JNP8Ntvv0kdjUqob9++WLFiBYYOHYrw8HAkJiZi/PjxiIiIkDoaldDYsWPx559/om3btujWrRuaNWsmdSSza968Odq1a2fa3rt3b5HtefPmSRFLNMqt20Qyo6TfhywsLGBra2va1uv1sLDgsoulkdFoLDLDW7duXQnT0H+xdOlSZGVlITo6GqtXr8aVK1fg5uaGL774QupoZjN16tQi2++//75ESV4OljSil0RJ50689dZb2LBhAwoKChAfH4+NGzcqco0iNahatSr27dsHjUaDu3fvIigoCK+99prUsegF2draolmzZrh58yZu3LiBkydPSh3JrHr27Pnc5wwfPhyrVq16CWnEx199iajEZs2aBYPBgDJlymDatGmwtbWFj4+P1LHoBfj6+iIyMhI3btxAx44dER8fD19fX6lj0QtYt24devfujZEjR0Kr1WLNmjWq/H9pMBikjmA2nEkjohKzsbHBhAkTFHPFmJqdPHkS/v7+ij7ZXC0MBgP8/PxQp04d7N69GxMnTsTZs2cVN5v2PEo6asH/lUQviZLOSXv09jMAcPfuXUXffkbJIiIi4Ovrq+iTzdViwIAB2LRpE0JDQ5GRkYERI0ZgyZIlUsei/4CHO4nM7Nq1a9i/fz8KCwtx9epV034lrR/23nvvYdmyZYiLi0NcXBy+++47tGvXDnPnzsWcOXOkjkclsHTpUmzfvh1NmjTB6tWr0blzZ/5gL2V2796Nzz77DP369UN6ejr8/f2h1+sxevRoVKlSRep49B+wpBGZ0fbt2/H555/jq6++Qnp6Ovr374+tW7cCAJycnCROZz6XLl0y3R8QeDCzduHCBUXdfkZNHp5s3qRJE1hZWanu8FhpN2bMGNPdBubOnYsWLVoo6pBfSSnpqAVLGpEZrVmzBr/88gtsbW1hZ2eHsLAwrF69WupYZvfwB0J2djaysrLwyy+/KO72M2rBk81Lv4iICNjb28PT0xP9+vXDTz/9hMLCQqljiWrHjh3Iy8t74mM9evR4uWFExJJGZEZqWT8sICAAMTEx+PDDD9GuXTvExcXhm2++QUxMDC8mKGUenmweGhqK6tWrY+LEiejVq5fUsagE6tWrh6lTp+LAgQMYNmwY4uLicOvWLQwbNgwHDhyQOp4oDh48CDc3N8yZMwenT58u8tjgwYOlCSUC3nGAyIymTp2Khg0bIjg4GAsWLMDGjRuRm5uLBQsWSB1NdLm5uShbtqzUMaiErl69+tjJ5p6enjyXqZS7ffs2wsPDER4erti7R+Tm5mLnzp3Ytm0b0tLS0LVrV/To0QN2dnZSRzMbljQiM8rOzsbKlSsRExMDo9EIZ2dnjBo1qsjsmhLs3bsXixcvRnZ2NgRBgNFoRE5ODo4cOSJ1NCqm3bt3Izg4GOfPn0eHDh3g5uaGmTNnYu/evVJHIyq2Y8eOISIiAkeOHEHjxo0RHx+Pjz76CB9//LHU0cyCJY2ISqxjx46YO3cu1q1bhxEjRiA6Oho5OTmYNWuW1NGomOrXr4/OnTtj3LhxqFmzJgCgffv22LNnj8TJiJ4vMDAQ27Ztw+uvv47evXvD1dUVZcqUQVZWFtq3b4+4uDipI5oF10kjMiO1rB9Wvnx5ODs748SJE8jMzMSkSZPQpUsXqWNRCURERCA0NBSenp6oXr06unbtqviTzUk5LCws8OOPP6JGjRpF9tva2mLNmjUSpTI/zqQRmdHEiRPh5uZmWp7iwIEDiIqKwsCBA+Hr64vg4GCJE5qHp6cn/Pz8cPHiRZw5cwZjx45F165dsXv3bqmjUQkVFBRg//79CA0NxcGDB+Hi4oIBAwagdevWUkcjeqr79+/j4MGDuHfvHgCgsLAQ165dU9TN5AFe3UlkVmpZP8zb2xuLFy9G27ZtERsbixYtWhQZN5UelpaW6NChA1asWIGDBw/C2dkZCxculDoW0TNNmDAB69evR2BgIH7//XcEBgYiKSlJ6lhmx5JGZEZqWT8sKSkJS5YsgbW1NbZs2YLo6GhMmTJF6lj0H1WpUgWffvqpYq8GJOW4cOEC1q9fj44dO2LIkCH45ZdfcP36daljmR1LGpEZqWX9sA0bNhTZrlixokRJiEiN7OzsoNFoULt2bVy4cAE1atRAfn6+1LHMjuekEYlMieuHDRkyBHl5eXj33XdRpkwZ0/7Ro0dLmIqI1GLmzJmwtrbG//3f/2HixIno0qULIiMjERkZKXU0s+LVnURmpJb1wxo3bix1BCJSsdmzZ+PkyZOoW7cuxowZg9jYWEWeS8mZNCIzUtP6YdnZ2bhy5Qrq1auH3Nxc2NjYSB2JiFRi+PDhaNu2Ldq0aYOqVatKHUc0PCeNyIwerh/27rvvmtYPU9osGgDExsaie/fuGDlyJNLS0tC2bVscOnRI6lhEpBKff/45UlNTMWbMGPTq1QtLlix57B6eSsCSRmRGZcuWxd9//406dergjz/+QF5eniJPZl20aBE2btyIChUqQKfTISgoCP7+/lLHIiKVaNy4McaMGYNVq1ahT58+2LJlCzw9PaWOZXYsaURmpJb1w4xGI3Q6nWm7bt26EqYhIrWZM2cOunXrhs8++wyXL1+Gj48PYmNjpY5ldrxwgMiMHq4fBgBbtmxBRkaGIpenqFq1Kvbt2weNRoO7d+8iKCgIr732mtSxiEgl7t69C0EQULt2bdSpUwdvvvkmypcvL3Uss+OFA0Rm5O7ujm3btkkdQ3RpaWnw8/NDTEwMBEFA8+bNMWPGDOj1eqmjEZGKJCUlITY2Fj///DOys7Px+++/Sx3JrDiTRmRGVatWhZeXl+LXDzt58iT8/f1haclvIUT08v3111+IjY1FbGwsEhIS4OjoqMj7zfI7LJEZqWX9sIiICPj6+qJt27bo1q0bmjVrJnUkIlKRMWPGoH379hg8eDCaNGkCrVYrdSRR8HAnkZmpZf2wrKwsREdHY8eOHbhy5Qrc3NzwxRdfSB2LiFSgR48eCA8PlzqG6Hh1J5EZqWn9MFtbWzRr1gxNmjSBlZUVTp48KXUkIlIJvV6PY8eOIS8vT+ooouJMGpEZ9e3bFytWrMDQoUMRHh6OxMREjB8/HhEREVJHM6t169Zh27ZtyMvLQ7du3dCtWzdkZ2ejdu3aUkcjIhVwdnZGenp6kX0ajQbx8fHSBBIJz0kjMiO1rB9mMBjg5+eHOnXqYPfu3Zg4cSLOnj3L2TQieimUeCeXJ2FJIzIjtawfNmDAAGzatAmhoaHIyMjAiBEjTOvDERGJJSQkBB999BG+/fbbJz6utCvpeU4akRn5+voiMjISN27cQMeOHREfHw9fX1+pY5nN7t278dlnn6Ffv35IT0+Hv78/9Ho9Ro8ejSpVqkgdj4gUTm1naPGcNCIzio6ORps2bRS7flj9+vXRuXNnjBs3DjVr1gQAtG/fHnv27JE4GRGpmSAIuHbtGmrUqCF1FLPiTBqRGUVERKBdu3bw8fHB8ePHpY5jdhEREbC3t4enpyf69euHn376CYWFhVLHIiKVCQkJQdOmTfH222/j7bffxjvvvINPP/1U6lhmx5k0IjNTw/phBQUF2L9/P0JDQ3Hw4EG4uLhgwIABilzxm4jkp127dvjpp5+wePFieHt748CBAzhx4gQWLlwodTSz4kwakZmpYf0wS0tLdOjQAStWrMDBgwfh7OysuG+ORCRfdnZ2qFGjBhwcHHDx4kUMGDAAFy5ckDqW2SnzxBkiifx7/bA1a9YgOztb6liiqlKlCj799FNFHmogInkqV64cjhw5AgcHB0RHR6NRo0bIzc2VOpbZcSaNyIwerh8WGhqK6tWrY+LEiejVq5fUsYiIFMFgMAAAZs6ciX379uHDDz9Eeno6OnfujI8//ljidObHc9KIzOjq1auPrR/m6enJ5SmIiMygZ8+eCAsLAwD88MMPip/B50wakRlw/TAiIvE9Oq8UGRkpYZKXg+ekEZnBmDFj0LlzZwQHB5vWD9NoNBKnIiJSlke/r6rhQCBLGpEZREREIDQ0FJ6enqhevTq6du3K9cOIiESkhl+EeU4akRlx/TAiIvE0bNgQ9vb2AB5cRPDwY0EQoNFoFHf3E5Y0IpHcvn0b4eHhCA8PR0REhNRxiIhKvevXrz/z8erVq7+kJC8HSxoRERGRDPHqTiIiIiIZYkkjIiIikiGWNCIiIiIZ4hIcRKQYDg4OqFevHiws/vf7Z8OGDeHn5/dCn+/06dPYvHkzfH19zRWRiKjYWNKISFF++ukns93lITEx0XSvQCKil40ljYhUISkpCX5+fkhPT0dhYSEGDhyIPn36wGg04uuvv8apU6dw7949CIKAr776Cq+99hqWLl2KzMxMfPnll+jRowfmzp2Lbdu2AQDi4uJM28uWLcOff/6JlJQUODg4ICAgACtXrsSuXbtgNBpRvXp1+Pj4wN7eHrt27cLKlSuh0Wig1WoxefJkvPfeexL/7RCRHLGkEZGiDBo0qMjhzh9++AEVK1bE2LFj4e/vjwYNGiAzMxMfffQR6tatC0EQkJKSgpCQEFhYWGD16tVYs2YNvvvuO4wdOxY7d+7EvHnzEBcX98z3vX79OrZt2wZLS0uEh4fj4sWL+PXXX2FpaYmQkBDMmDEDa9asgb+/PwICAtC4cWMcOnQIcXFxLGlE9EQsaUSkKE863JmYmIgrV65g2rRppn25ubk4f/48PD09UbFiRQQHB+Pq1auIi4vDK6+8UuL3bdy4MSwtH3xL3bdvH86cOYPevXsDAIxGI3JycgAAXbt2xejRo9G6dWu0aNECQ4cOfdGhEpHCsaQRkeIVFhaifPny2Lp1q2nfrVu3UL58eezfvx9+fn745JNP0L59e7z55ptPvEOERqMpckPn/Pz8Io/b2NiYPjYajRgyZAg8PT0BAHl5ecjIyAAAeHt7o3fv3jh8+DBCQ0Pxww8/YPPmzWYdLxEpA5fgICLFq127NsqWLWsqaTdu3IC7uzvOnj2Lw4cPo23btvD09ETDhg0RHR2NwsJCAIBWq0VBQQEAoEqVKkhOTkZaWhoEQcBvv/321Pdr2bIlNm/ejKysLADAkiVLMHnyZBQUFKBdu3bIycnB//3f/8HHxwcXLlxAXl6eyH8DRFQacSaNiBTP2toaK1asgJ+fH9auXYuCggJ88cUXaNasGSpVqoQJEybAw8MDBQUFaNGihemE/8aNG2P58uUYPXo0vv32W/Tv3x+9e/eGTqdDmzZtcObMmSe+X9++fWEwGNCvXz9oNBpUq1YN8+fPh6WlJaZNm4aJEyfC0tISGo0GX3/9NaytrV/y3wgRlQa8dycRERGRDPFwJxEREZEMsaQRERERyRBLGhEREZEMsaQRERERyRBLGhEREZEMsaQRERERyRBLGhEREZEMsaQRERERydD/A7HxGp8EGP/WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the feature importances\n",
    "importances = d_tree_best.feature_importances_\n",
    "\n",
    "# Create an index of the feature importances, in descending order\n",
    "sorted_index = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the feature importances, using the sorted index\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(range(x_train.shape[1]), importances[sorted_index], color='#006600', edgecolor='black')\n",
    "plt.xticks(range(x_train.shape[1]), x_train.columns[sorted_index], rotation=90)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40050458-2991-47a2-ad0b-9b5d3916cd3d",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* Feature importance (supported by tree visualization) show that:\n",
    "    * Strokes Gained Approach is the highest determining feature for golf scores.\n",
    "    * Strokes Gained Off The Tee and Average Scrambling show significant impact for golf scores.\n",
    "    * Strokes Gained Putts and Strokes Gained Around The Green show minor impact for golf scores.\n",
    "    * Average Putts, Greens in regulation, driving distance, and fairway percentages show the minimal impact for golf scores. \n",
    "* **This, along with insight drawn from EDA, confirms a number of our objective questions including**:\n",
    "    * We can see the top predicting features for score, and therefore the skills golfers should prioritize first, are approach shots, tee shots, and then scrambling. \n",
    "    * Between the strokes gained metrics and traditional metrics, strokes gained is the clear winner. With all traditional metrics having little to no impact other than average scrambling. \n",
    "    * The traditional view that short game is a higher priority than long game is also debunked, with long game metrics holding the top 2 positions. \n",
    "    * The hypothesis that fairways percentage is a missleading metric is also confirmed with it holding the lowest ranking position. \n",
    "\n",
    "**These insights, along with EDA, answer all our objective questions.** Howerver we should build a more generalized and accuracte model to solidify these claims. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d249173-d940-4965-9816-6c56b108beae",
   "metadata": {},
   "source": [
    "### **Random Forest Classifier - Base Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f79a2d2e-0045-4b1f-a3ea-c6ba18c017bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid to search\n",
    "param_grid = {'n_estimators': [10, 50, 100],\n",
    "              'max_depth': [None, 5, 10],\n",
    "              'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "# Create the random forest regressor model\n",
    "rf_1 = RandomForestRegressor(random_state=1)\n",
    "\n",
    "# Create the GridSearchCV object with a 5-fold cross-validation\n",
    "grid_rf = GridSearchCV(rf_1, param_grid, cv=5, return_train_score=True)\n",
    "\n",
    "# Fit the GridSearchCV object to the training data\n",
    "grid_rf.fit(x_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters found by GridSearchCV\n",
    "print(\"Best parameters: \", grid_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "214baa52-cf27-410d-86df-04dfb8cb92d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy:  0.9796996884358099\n",
      "Testing accuracy:  0.8590898953534397\n"
     ]
    }
   ],
   "source": [
    "# Get the training and testing accuracy\n",
    "training_accuracy = grid_rf.score(x_train, y_train)\n",
    "testing_accuracy = grid_rf.score(x_test, y_test)\n",
    "\n",
    "# Print the training and testing accuracy\n",
    "print(\"Training accuracy: \", training_accuracy)\n",
    "print(\"Testing accuracy: \", testing_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbb285d2-272d-4446-8003-d19490841630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d_tree_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.66512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d_tree_2</td>\n",
       "      <td>0.898392</td>\n",
       "      <td>0.682547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rf_1</td>\n",
       "      <td>0.9797</td>\n",
       "      <td>0.85909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model_Name Training_Accuracy Testing_Accuracy\n",
       "0   d_tree_1               1.0          0.66512\n",
       "1   d_tree_2          0.898392         0.682547\n",
       "2       rf_1            0.9797          0.85909"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.append({'Model_Name':'rf_1',\n",
    "                          'Training_Accuracy':0.9796996884358099,\n",
    "                          'Testing_Accuracy':0.8590898953534397,},\n",
    "                            ignore_index=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ebc5137-11c7-4441-b630-6110ee04689b",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* Overall accuracy has improved with the random forest model with a testing accuracy of 86%.\n",
    "* The model is less overfitted with only a 12% difference. \n",
    "\n",
    "**We can try and tune the model to reduce overfitting**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0b8e38-2802-4ff7-8007-f32aca6086c2",
   "metadata": {},
   "source": [
    "### **Random Forest Classifier - Tuned Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ee5522f-ee1c-484e-91e3-780fc7dc6d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'bootstrap': True, 'max_depth': 10, 'max_leaf_nodes': 30, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "# Create the parameter grid\n",
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [5, 10, 15],\n",
    "    'n_estimators': [100, 200, 500],  # Number of trees in the random forest\n",
    "    'min_samples_split': [2, 4, 6],  # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': [2, 4, 6],  # Minimum number of samples required to be at a leaf node\n",
    "    'max_leaf_nodes': [10, 20, 30], # Maximum number of leaf nodes\n",
    "}\n",
    "\n",
    "\n",
    "# Create the RandomForestRegressor object\n",
    "rf_2 = RandomForestRegressor(random_state=1)\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_rf_2 = GridSearchCV(estimator=rf_2, param_grid=param_grid, cv=3, n_jobs=-1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_rf_2.fit(x_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters found by GridSearchCV\n",
    "print(\"Best parameters: \", grid_rf_2.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "726b3ddb-8893-4481-ac00-0a956cce8267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy:  0.8688388287495241\n",
      "Testing accuracy:  0.796666461082127\n"
     ]
    }
   ],
   "source": [
    "# Get the training and testing accuracy\n",
    "training_accuracy = grid_rf_2.score(x_train, y_train)\n",
    "testing_accuracy = grid_rf_2.score(x_test, y_test)\n",
    "\n",
    "# Print the training and testing accuracy\n",
    "print(\"Training accuracy: \", training_accuracy)\n",
    "print(\"Testing accuracy: \", testing_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0abb7a25-a477-41a5-a893-efdd9d24fb45",
   "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>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d_tree_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.66512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d_tree_2</td>\n",
       "      <td>0.898392</td>\n",
       "      <td>0.682547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rf_1</td>\n",
       "      <td>0.9797</td>\n",
       "      <td>0.85909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rf_2</td>\n",
       "      <td>0.868839</td>\n",
       "      <td>0.796666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model_Name Training_Accuracy Testing_Accuracy\n",
       "0   d_tree_1               1.0          0.66512\n",
       "1   d_tree_2          0.898392         0.682547\n",
       "2       rf_1            0.9797          0.85909\n",
       "3       rf_2          0.868839         0.796666"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.append({'Model_Name':'rf_2',\n",
    "                          'Training_Accuracy':0.8688388287495241,\n",
    "                          'Testing_Accuracy':0.796666461082127,},\n",
    "                            ignore_index=True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5a0f86-ccca-4dc6-ae71-4d534f3d705f",
   "metadata": {},
   "source": [
    "Cant reduce the ~10% gap between the training accuraccy and the testing accuracy. Attempted multiple tuning methods to reduce overfitting and all reduced overall accuracy but maintained the gap. This likely means there is little improvement that can be gained from further tuning. The slight overfitting is likely due to the reletively small data size of 1678. Further improvement would have to come from gathering more data. \n",
    "\n",
    "* While the overfitted gap has been reduced from 12% to 7% the testing accuracy has fallend by 6%. \n",
    "* It is better therefore to **continue forward with rf_1 as it has the greater potntial to generalize well.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1d6d51c0-bd7a-4ef1-ba95-9adeb28fb6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07217300000000004"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.868839-0.796666"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c45efcd-2fe0-4632-aa37-4ba1afdd9b2d",
   "metadata": {},
   "source": [
    "#### **Feature Importance for rf_1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1560993e-56ae-48b4-9f99-b9e233dea4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit a RandomForestRegressor object with the best hyperparameters\n",
    "rf_best = RandomForestRegressor(random_state=1, **grid_rf.best_params_)\n",
    "rf_best.fit(x_train, y_train)\n",
    "\n",
    "# Plot the feature importance\n",
    "importance = rf_best.feature_importances_\n",
    "sorted_index = importance.argsort()[::-1]\n",
    "\n",
    "# Map the original feature names to their desired labels\n",
    "feature_labels = {\n",
    "    'Fairway_Percentage': 'Fairway %',\n",
    "    'Avg_Distance': 'Average Distance',\n",
    "    'gir': 'Greens In Regulation',\n",
    "    'Average_Putts': 'Average Putts',\n",
    "    'Average_Scrambling': 'Average Scrambling',\n",
    "    'Average_SG_Putts': 'Strokes Gained Putts',\n",
    "    'SG:OTT': 'Strokes Gained Off The Tee',\n",
    "    'SG:APR': 'Strokes Gained Approach Shots',\n",
    "    'SG:ARG': 'Strokes Gained Around The Green'\n",
    "}\n",
    "\n",
    "# Create a list of the labeled feature names\n",
    "labeled_features = [feature_labels[feature] for feature in x_train.columns[sorted_index]]\n",
    "\n",
    "color_map = ['red' if feature in ['Fairway %', 'Average Distance', 'Greens In Regulation', 'Average Putts', 'Average Scrambling'] \n",
    "             else 'green' for feature in labeled_features]\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "for i, v in enumerate(importance[sorted_index]):\n",
    "    plt.bar(i, v, color=color_map[i], edgecolor='black', alpha=0.5, linewidth=3)\n",
    "    plt.text(i - 0.25, v + 0.01, '{:.2f}'.format(v), color='black')\n",
    "\n",
    "plt.xticks(range(x_train.shape[1]), labeled_features, rotation=90)\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.title(\"Key Factors in Score Prediction\", fontsize=20, fontweight='bold', fontstyle='oblique')\n",
    "\n",
    "# Save the visualization to a file\n",
    "plt.savefig(\"Key Factors in Score Prediction.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "814d6863-d8f4-462d-b1bb-1eaa30be1963",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fairway_Percentage \n",
    "Avg_Distance  \n",
    "gir             \n",
    "Average_Putts   \n",
    "Average_Scrambling \n",
    "Average_SG_Putts  \n",
    "SG:OTT        \n",
    "SG:APR   \n",
    "SG:ARG      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba656af-bd28-420e-b346-412948909eef",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* Feature importance shows no change from our tuned decision tree model to our random forest model. This supports our above observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fe8c3f-26a0-4532-9e1d-4d1e567c838e",
   "metadata": {},
   "source": [
    "## **Conclusion and Recommendations**\n",
    "### **Reminder of Objectives:**\n",
    "* Build a prediction model for sucess in golf measured by average golf score.\n",
    "* Determine the most impactfull features that determine a players golf scores.\n",
    "* Compare the impact of strokes gained stats and traditional stats on a players score.\n",
    "* Investigate the effect of short game vs long game skills on a players score.\n",
    "* Investigate the impact of driving accuracy vs driving distance on a players score.\n",
    "\n",
    "### **Conclusion:**\n",
    "#### **Model Performance**\n",
    "We built multiple decision tree based models that can predict average_score, we can see from the table below they generally **performed well** but with varying degrees **overfitted**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "225e0719-7b0f-4e4e-aaa1-b5e9d9dbf506",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model_Name</th>\n",
       "      <th>Training_Accuracy</th>\n",
       "      <th>Testing_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d_tree_1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.66512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d_tree_2</td>\n",
       "      <td>0.898392</td>\n",
       "      <td>0.682547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rf_1</td>\n",
       "      <td>0.9797</td>\n",
       "      <td>0.85909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rf_2</td>\n",
       "      <td>0.868839</td>\n",
       "      <td>0.796666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model_Name Training_Accuracy Testing_Accuracy\n",
       "0   d_tree_1               1.0          0.66512\n",
       "1   d_tree_2          0.898392         0.682547\n",
       "2       rf_1            0.9797          0.85909\n",
       "3       rf_2          0.868839         0.796666"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c574d01-606f-4a8b-a50c-370b12db60e9",
   "metadata": {},
   "source": [
    "Both the random forest models rf_1 and rf_2 performed well with:\n",
    "    * rf_1 being overall more accurate\n",
    "    * rf_2 being slighlty less overfitted\n",
    "**The best option moving forward for a generalized model is likely rf_1 giving the greater accuracy of 86%.**\n",
    "\n",
    "#### **Feature Importance**\n",
    "Feature importance showed that:\n",
    "   * **Strokes Gained Approach** is the **highest impact** feature for determining a players score.\n",
    "   * Strokes Gained Off The Tee and Average Scrambling show **significant impact** for a players score.\n",
    "   * Strokes Gained Putts and Strokes Gained Around The Green show **minor impact** for a players score.\n",
    "   * Average Putts, Greens in regulation, driving distance, and fairway percentages showed **very little impact** for a players score. \n",
    "    \n",
    "    \n",
    "This, along with insight drawn from EDA, answers a number of our objective questions:\n",
    "   * We can see the **top impact features** for a players score, and therefore the **skills golfers should prioritize** first, are **approach shots, tee shots, and then scrambling**.\n",
    "      * Although it is likely that scrambling only performed better than SG-Putts and SG-Around the green because it is a sudo combination of those to metrics that is biased towards better play. \n",
    "  * Between the strokes gained stats and traditional stats, **strokes gained is clearly more impactfull**. With **traditional metrics having little to no impact** other than average scrambling. \n",
    "  * The traditional view that short game is a higher priority than long game is also debunked, with **long game metrics holding the top 2 positions**. \n",
    "  * The hypothesis that **fairways percentage** is a missleading metric is also confirmed with it holding the **lowest ranking position**.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a641f5cc-0e4b-4a01-8d6e-489d946532f9",
   "metadata": {},
   "source": [
    "### **Recommendations:**\n",
    "**Model Building**\n",
    "* While our model performs with a high accuracy of 86% and will likely generalize well, there still **room for improvement in both accuracy and overfitting**. Given the training and testing gap remained even with many itterations of tuning, we speculated these issues are likely coming from having a relatively **small data set of 1,678**. To further improve our model it is recomended:\n",
    "\n",
    "**Increase our data** set by finding additional data bases to draw from and merge with our current dataset.\n",
    "  * We can do this by sampling from PGA tour data outside of the 2010 to 2018 seasons\n",
    "       * However we should perhaps avoid the covid season of 2020-2021 as the data could be biased\n",
    "  * A better option to increase our data would be to sample from outside of the PGA tour. Not only would this give us more data but a greater range of player skill levels and therefore more diverse data. The best option for this would likely come from the ameture and college golf leagues as the recording of data is held to a high efficacy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50c8445-51a0-4a31-9baf-989bda880d37",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Beyond the insights derived from our model it also has many practical applications. We can easily use this to predict a players performance given their past years stats, and therefore show players what the impact improvements in each stat would have. \n",
    "\n",
    "It should also be noted that while Strokes Gained stats have prooven to be of higher impact, they are not as practical to record as the more traditional stats. However, there is potential to train other models using this data to predict the strokes gained stats using the more traditional stats, specifically:\n",
    "  * SG Putts could be derived from gir, Average_Putts, and Average_Scrambling.\n",
    "  * SG ARG could be derived from gir, Average_Putts, and Average_Scrambling.\n",
    "  * SG-APR could be derived from Avg_Distance, Fairway_Percentage.\n",
    "  * SG-OTT could be derived from Avg_Distance, Fairway_Percentage.\n",
    "\n",
    "It would likely take additional features to do this with the needed accuracy but we could then use all the models in combination to take easily measured stats, to predict strokes gained stats, and then average score. For the everyday player this would provide a valuable system that would show them how to best improve at the game. "
   ]
  },
  {
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   "execution_count": null,
   "id": "06c25507-8e90-406d-bed2-b3b6aab3de96",
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