eda-and-lr-nba-data-height-weight-dreb-age.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/nishitkaul88/eda-and-lr-nba-data-height-weight-dreb-age?scriptVersionId=160530798\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"397991b7","metadata":{"papermill":{"duration":0.015454,"end_time":"2024-01-26T16:21:46.889424","exception":false,"start_time":"2024-01-26T16:21:46.87397","status":"completed"},"tags":[]},"source":["# EDA and Linear Regression on NBA-Data\n","Forget high-fives and alley-oops, this data-driven slam dunk is all about understanding player performance through the lens of Linear Regression. Today, we enter the statistical arena, ready to dissect the relationship between physical attributes and on-court prowess.\n","\n"," We'll start by isolating individual variables, like height and weight, and analyzing their impact on a key metric: Defensive Rebounding Percentage (DRB%). Imagine it as a statistical microscope, zooming in to see how these basic physical traits influence a player's ability to secure those crucial defensive boards. Linear regression will be our tool, crafting equations that quantify these relationships, revealing if taller players truly rule the glass and if heavier frames translate to rebounding dominance.\n","\n"," But the fun doesn't stop there! We'll then level up to a full-court press, incorporating both height and weight into our model. Think of it as a statistical double-team, capturing the combined effect of these physical attributes on DRB%. Will the combined influence reveal even stronger trends, or will unexpected interactions emerge, challenging our initial assumptions?\n","\n"," This notebook is your courtside seat to a data-driven analysis, where statistics replace trash talk and regression models dominate the headlines. We'll:\n","\n","1. Isolating individual variables: Analyze the one-on-one relationship between height, weight, and DRB%, using multiple regression as our statistical weapon.\n","\n","2. Unveiling multivariable magic: Combine height and weight in a full-court press regression model, exploring the intricate interplay between these factors and their impact on defensive rebounding.\n","\n","3. Identifying trends and secrets: Interpret the regression models, uncovering the hidden narratives within the data and potentially discovering unique insights into player performance.\n"," So, grab your data jerseys and statistical sneakers, because we're about to step onto the court of analysis! Prepare to witness the power of linear regression as it cracks the code of NBA performance, one equation at a time.\n","\n"," Let the data play begin!"]},{"cell_type":"markdown","id":"042b32bb","metadata":{"papermill":{"duration":0.009439,"end_time":"2024-01-26T16:21:46.911554","exception":false,"start_time":"2024-01-26T16:21:46.902115","status":"completed"},"tags":[]},"source":[" **Here's a detailed description of the packages used in this NBA analysis:**\n","\n","**Core Data Manipulation and Visualization:**\n","\n","- **numpy**: Provides efficient numerical computations and array operations for handling the NBA data.\n","- **pandas**: Creates and manipulates DataFrames, enabling easy data exploration and cleaning.\n","- **matplotlib.pyplot**: Generates informative visualizations like plots and charts to visualize trends and relationships within the data.\n","- **seaborn**: Enhances visual clarity and aesthetics, building upon matplotlib for visually appealing statistical graphics.\n","\n","**Machine Learning:**\n","\n","- **sklearn.model_selection**: Provides tools for splitting data into training and testing sets, implementing cross-validation, and evaluating model performance.\n","- **sklearn.linear_model**: Houses various linear regression models, including LinearRegression, Lasso, and Ridge, for building predictive models.\n","\n","**Evaluation and Diagnostics:**\n","\n","- **sklearn.metrics**: Offers a suite of metrics to assess model performance, such as mean squared error, R-squared, and more.\n","- **scipy.stats.mstats**: Contains statistical tests, including normaltest, to check for normality in data distributions, a crucial assumption for linear regression.\n","- **scipy.stats**: Provides additional statistical tools, like the boxcox function for transforming non-normal data to better suit linear regression assumptions.\n","\n","**Data Preprocessing:**\n","\n","- **sklearn.preprocessing**: Includes techniques for data scaling and transformation, such as StandardScaler, which standardizes features to have a mean of 0 and a standard deviation of 1, often enhancing model performance.\n","- **PolynomialFeatures**: Generates polynomial features, allowing for exploration of non-linear relationships between variables.\n","\n","**Model Building and Refinement:**\n","\n","- **sklearn.pipeline**: Streamlines the modeling process by creating a sequence of steps, including preprocessing and model fitting, making model development more efficient.\n","- **sklearn.model_selection.GridSearchCV**: Automates hyperparameter tuning, searching for optimal model configurations to improve model performance.\n","\n","**3D Visualization:**\n","\n","- **mpl_toolkits.mplot3d**: Enables the creation of 3D plots, particularly useful for visualizing relationships between multiple variables in a more intuitive and immersive manner.\n"]},{"cell_type":"code","execution_count":1,"id":"1ee287f2","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:46.937717Z","iopub.status.busy":"2024-01-26T16:21:46.937196Z","iopub.status.idle":"2024-01-26T16:21:49.249733Z","shell.execute_reply":"2024-01-26T16:21:49.248566Z"},"papermill":{"duration":2.331583,"end_time":"2024-01-26T16:21:49.252829","exception":false,"start_time":"2024-01-26T16:21:46.921246","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import seaborn as sea\n","from sklearn.model_selection import train_test_split, KFold, cross_val_predict\n","from sklearn.linear_model import LinearRegression, Lasso, Ridge\n","from sklearn import metrics\n","from scipy.stats.mstats import normaltest\n","from scipy.stats import boxcox\n","from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n","from sklearn.metrics import r2_score\n","from sklearn.pipeline import Pipeline\n","from sklearn.model_selection import GridSearchCV\n","import warnings"]},{"cell_type":"markdown","id":"9e58e19d","metadata":{"papermill":{"duration":0.009584,"end_time":"2024-01-26T16:21:49.272294","exception":false,"start_time":"2024-01-26T16:21:49.26271","status":"completed"},"tags":[]},"source":["### Loading the data"]},{"cell_type":"code","execution_count":2,"id":"e5b7120d","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.294402Z","iopub.status.busy":"2024-01-26T16:21:49.293836Z","iopub.status.idle":"2024-01-26T16:21:49.448446Z","shell.execute_reply":"2024-01-26T16:21:49.447295Z"},"papermill":{"duration":0.16905,"end_time":"2024-01-26T16:21:49.451048","exception":false,"start_time":"2024-01-26T16:21:49.281998","status":"completed"},"tags":[]},"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>Unnamed: 0</th>\n","      <th>player_name</th>\n","      <th>team_abbreviation</th>\n","      <th>age</th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>college</th>\n","      <th>country</th>\n","      <th>draft_year</th>\n","      <th>draft_round</th>\n","      <th>...</th>\n","      <th>pts</th>\n","      <th>reb</th>\n","      <th>ast</th>\n","      <th>net_rating</th>\n","      <th>oreb_pct</th>\n","      <th>dreb_pct</th>\n","      <th>usg_pct</th>\n","      <th>ts_pct</th>\n","      <th>ast_pct</th>\n","      <th>season</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Randy Livingston</td>\n","      <td>HOU</td>\n","      <td>22.0</td>\n","      <td>193.04</td>\n","      <td>94.800728</td>\n","      <td>Louisiana State</td>\n","      <td>USA</td>\n","      <td>1996</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>3.9</td>\n","      <td>1.5</td>\n","      <td>2.4</td>\n","      <td>0.3</td>\n","      <td>0.042</td>\n","      <td>0.071</td>\n","      <td>0.169</td>\n","      <td>0.487</td>\n","      <td>0.248</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Gaylon Nickerson</td>\n","      <td>WAS</td>\n","      <td>28.0</td>\n","      <td>190.50</td>\n","      <td>86.182480</td>\n","      <td>Northwestern Oklahoma</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>3.8</td>\n","      <td>1.3</td>\n","      <td>0.3</td>\n","      <td>8.9</td>\n","      <td>0.030</td>\n","      <td>0.111</td>\n","      <td>0.174</td>\n","      <td>0.497</td>\n","      <td>0.043</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>George Lynch</td>\n","      <td>VAN</td>\n","      <td>26.0</td>\n","      <td>203.20</td>\n","      <td>103.418976</td>\n","      <td>North Carolina</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>8.3</td>\n","      <td>6.4</td>\n","      <td>1.9</td>\n","      <td>-8.2</td>\n","      <td>0.106</td>\n","      <td>0.185</td>\n","      <td>0.175</td>\n","      <td>0.512</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>George McCloud</td>\n","      <td>LAL</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Florida State</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>10.2</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>-2.7</td>\n","      <td>0.027</td>\n","      <td>0.111</td>\n","      <td>0.206</td>\n","      <td>0.527</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>George Zidek</td>\n","      <td>DEN</td>\n","      <td>23.0</td>\n","      <td>213.36</td>\n","      <td>119.748288</td>\n","      <td>UCLA</td>\n","      <td>USA</td>\n","      <td>1995</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>0.3</td>\n","      <td>-14.1</td>\n","      <td>0.102</td>\n","      <td>0.169</td>\n","      <td>0.195</td>\n","      <td>0.500</td>\n","      <td>0.064</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>Gerald Wilkins</td>\n","      <td>ORL</td>\n","      <td>33.0</td>\n","      <td>198.12</td>\n","      <td>102.058200</td>\n","      <td>Tennessee-Chattanooga</td>\n","      <td>USA</td>\n","      <td>1985</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>2.2</td>\n","      <td>2.2</td>\n","      <td>-5.8</td>\n","      <td>0.031</td>\n","      <td>0.064</td>\n","      <td>0.203</td>\n","      <td>0.503</td>\n","      <td>0.143</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>Gheorghe Muresan</td>\n","      <td>WAS</td>\n","      <td>26.0</td>\n","      <td>231.14</td>\n","      <td>137.438376</td>\n","      <td>NaN</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>6.6</td>\n","      <td>0.4</td>\n","      <td>6.9</td>\n","      <td>0.098</td>\n","      <td>0.217</td>\n","      <td>0.185</td>\n","      <td>0.618</td>\n","      <td>0.024</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>Glen Rice</td>\n","      <td>CHH</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>99.790240</td>\n","      <td>Michigan</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>26.8</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>3.2</td>\n","      <td>0.025</td>\n","      <td>0.087</td>\n","      <td>0.272</td>\n","      <td>0.605</td>\n","      <td>0.088</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>Glenn Robinson</td>\n","      <td>MIL</td>\n","      <td>24.0</td>\n","      <td>200.66</td>\n","      <td>106.594120</td>\n","      <td>Purdue</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>21.1</td>\n","      <td>6.3</td>\n","      <td>3.1</td>\n","      <td>-2.9</td>\n","      <td>0.051</td>\n","      <td>0.144</td>\n","      <td>0.278</td>\n","      <td>0.528</td>\n","      <td>0.146</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>9</td>\n","      <td>Grant Hill</td>\n","      <td>DET</td>\n","      <td>24.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Duke</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>21.4</td>\n","      <td>9.0</td>\n","      <td>7.3</td>\n","      <td>6.9</td>\n","      <td>0.049</td>\n","      <td>0.232</td>\n","      <td>0.283</td>\n","      <td>0.556</td>\n","      <td>0.356</td>\n","      <td>1996-97</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10 rows × 22 columns</p>\n","</div>"],"text/plain":["   Unnamed: 0       player_name team_abbreviation   age  player_height  \\\n","0           0  Randy Livingston               HOU  22.0         193.04   \n","1           1  Gaylon Nickerson               WAS  28.0         190.50   \n","2           2      George Lynch               VAN  26.0         203.20   \n","3           3    George McCloud               LAL  30.0         203.20   \n","4           4      George Zidek               DEN  23.0         213.36   \n","5           5    Gerald Wilkins               ORL  33.0         198.12   \n","6           6  Gheorghe Muresan               WAS  26.0         231.14   \n","7           7         Glen Rice               CHH  30.0         203.20   \n","8           8    Glenn Robinson               MIL  24.0         200.66   \n","9           9        Grant Hill               DET  24.0         203.20   \n","\n","   player_weight                college country draft_year draft_round  ...  \\\n","0      94.800728        Louisiana State     USA       1996           2  ...   \n","1      86.182480  Northwestern Oklahoma     USA       1994           2  ...   \n","2     103.418976         North Carolina     USA       1993           1  ...   \n","3     102.058200          Florida State     USA       1989           1  ...   \n","4     119.748288                   UCLA     USA       1995           1  ...   \n","5     102.058200  Tennessee-Chattanooga     USA       1985           2  ...   \n","6     137.438376                    NaN     USA       1993           2  ...   \n","7      99.790240               Michigan     USA       1989           1  ...   \n","8     106.594120                 Purdue     USA       1994           1  ...   \n","9     102.058200                   Duke     USA       1994           1  ...   \n","\n","    pts  reb  ast  net_rating  oreb_pct  dreb_pct  usg_pct  ts_pct  ast_pct  \\\n","0   3.9  1.5  2.4         0.3     0.042     0.071    0.169   0.487    0.248   \n","1   3.8  1.3  0.3         8.9     0.030     0.111    0.174   0.497    0.043   \n","2   8.3  6.4  1.9        -8.2     0.106     0.185    0.175   0.512    0.125   \n","3  10.2  2.8  1.7        -2.7     0.027     0.111    0.206   0.527    0.125   \n","4   2.8  1.7  0.3       -14.1     0.102     0.169    0.195   0.500    0.064   \n","5  10.6  2.2  2.2        -5.8     0.031     0.064    0.203   0.503    0.143   \n","6  10.6  6.6  0.4         6.9     0.098     0.217    0.185   0.618    0.024   \n","7  26.8  4.0  2.0         3.2     0.025     0.087    0.272   0.605    0.088   \n","8  21.1  6.3  3.1        -2.9     0.051     0.144    0.278   0.528    0.146   \n","9  21.4  9.0  7.3         6.9     0.049     0.232    0.283   0.556    0.356   \n","\n","    season  \n","0  1996-97  \n","1  1996-97  \n","2  1996-97  \n","3  1996-97  \n","4  1996-97  \n","5  1996-97  \n","6  1996-97  \n","7  1996-97  \n","8  1996-97  \n","9  1996-97  \n","\n","[10 rows x 22 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["nba_data = pd.read_csv(\"/kaggle/input/nba-players-data/all_seasons.csv\")\n","\n","# nba_data (You can run this to check the maximum data) It has over 12843 players\n","\n","nba_data.head(10)"]},{"cell_type":"code","execution_count":3,"id":"db2c4c03","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.474122Z","iopub.status.busy":"2024-01-26T16:21:49.473722Z","iopub.status.idle":"2024-01-26T16:21:49.506456Z","shell.execute_reply":"2024-01-26T16:21:49.505267Z"},"papermill":{"duration":0.047506,"end_time":"2024-01-26T16:21:49.508908","exception":false,"start_time":"2024-01-26T16:21:49.461402","status":"completed"},"tags":[]},"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>Unnamed: 0</th>\n","      <th>player_name</th>\n","      <th>team_abbreviation</th>\n","      <th>age</th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>college</th>\n","      <th>country</th>\n","      <th>draft_year</th>\n","      <th>draft_round</th>\n","      <th>...</th>\n","      <th>pts</th>\n","      <th>reb</th>\n","      <th>ast</th>\n","      <th>net_rating</th>\n","      <th>oreb_pct</th>\n","      <th>dreb_pct</th>\n","      <th>usg_pct</th>\n","      <th>ts_pct</th>\n","      <th>ast_pct</th>\n","      <th>season</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Randy Livingston</td>\n","      <td>HOU</td>\n","      <td>22.0</td>\n","      <td>193.04</td>\n","      <td>94.800728</td>\n","      <td>Louisiana State</td>\n","      <td>USA</td>\n","      <td>1996</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>3.9</td>\n","      <td>1.5</td>\n","      <td>2.4</td>\n","      <td>0.3</td>\n","      <td>0.042</td>\n","      <td>0.071</td>\n","      <td>0.169</td>\n","      <td>0.487</td>\n","      <td>0.248</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Gaylon Nickerson</td>\n","      <td>WAS</td>\n","      <td>28.0</td>\n","      <td>190.50</td>\n","      <td>86.182480</td>\n","      <td>Northwestern Oklahoma</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>3.8</td>\n","      <td>1.3</td>\n","      <td>0.3</td>\n","      <td>8.9</td>\n","      <td>0.030</td>\n","      <td>0.111</td>\n","      <td>0.174</td>\n","      <td>0.497</td>\n","      <td>0.043</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>George Lynch</td>\n","      <td>VAN</td>\n","      <td>26.0</td>\n","      <td>203.20</td>\n","      <td>103.418976</td>\n","      <td>North Carolina</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>8.3</td>\n","      <td>6.4</td>\n","      <td>1.9</td>\n","      <td>-8.2</td>\n","      <td>0.106</td>\n","      <td>0.185</td>\n","      <td>0.175</td>\n","      <td>0.512</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>George McCloud</td>\n","      <td>LAL</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Florida State</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>10.2</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>-2.7</td>\n","      <td>0.027</td>\n","      <td>0.111</td>\n","      <td>0.206</td>\n","      <td>0.527</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>George Zidek</td>\n","      <td>DEN</td>\n","      <td>23.0</td>\n","      <td>213.36</td>\n","      <td>119.748288</td>\n","      <td>UCLA</td>\n","      <td>USA</td>\n","      <td>1995</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>0.3</td>\n","      <td>-14.1</td>\n","      <td>0.102</td>\n","      <td>0.169</td>\n","      <td>0.195</td>\n","      <td>0.500</td>\n","      <td>0.064</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>Gerald Wilkins</td>\n","      <td>ORL</td>\n","      <td>33.0</td>\n","      <td>198.12</td>\n","      <td>102.058200</td>\n","      <td>Tennessee-Chattanooga</td>\n","      <td>USA</td>\n","      <td>1985</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>2.2</td>\n","      <td>2.2</td>\n","      <td>-5.8</td>\n","      <td>0.031</td>\n","      <td>0.064</td>\n","      <td>0.203</td>\n","      <td>0.503</td>\n","      <td>0.143</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>Gheorghe Muresan</td>\n","      <td>WAS</td>\n","      <td>26.0</td>\n","      <td>231.14</td>\n","      <td>137.438376</td>\n","      <td>NaN</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>6.6</td>\n","      <td>0.4</td>\n","      <td>6.9</td>\n","      <td>0.098</td>\n","      <td>0.217</td>\n","      <td>0.185</td>\n","      <td>0.618</td>\n","      <td>0.024</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>Glen Rice</td>\n","      <td>CHH</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>99.790240</td>\n","      <td>Michigan</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>26.8</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>3.2</td>\n","      <td>0.025</td>\n","      <td>0.087</td>\n","      <td>0.272</td>\n","      <td>0.605</td>\n","      <td>0.088</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>Glenn Robinson</td>\n","      <td>MIL</td>\n","      <td>24.0</td>\n","      <td>200.66</td>\n","      <td>106.594120</td>\n","      <td>Purdue</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>21.1</td>\n","      <td>6.3</td>\n","      <td>3.1</td>\n","      <td>-2.9</td>\n","      <td>0.051</td>\n","      <td>0.144</td>\n","      <td>0.278</td>\n","      <td>0.528</td>\n","      <td>0.146</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>9</td>\n","      <td>Grant Hill</td>\n","      <td>DET</td>\n","      <td>24.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Duke</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>21.4</td>\n","      <td>9.0</td>\n","      <td>7.3</td>\n","      <td>6.9</td>\n","      <td>0.049</td>\n","      <td>0.232</td>\n","      <td>0.283</td>\n","      <td>0.556</td>\n","      <td>0.356</td>\n","      <td>1996-97</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10 rows × 22 columns</p>\n","</div>"],"text/plain":["   Unnamed: 0       player_name team_abbreviation   age  player_height  \\\n","0           0  Randy Livingston               HOU  22.0         193.04   \n","1           1  Gaylon Nickerson               WAS  28.0         190.50   \n","2           2      George Lynch               VAN  26.0         203.20   \n","3           3    George McCloud               LAL  30.0         203.20   \n","4           4      George Zidek               DEN  23.0         213.36   \n","5           5    Gerald Wilkins               ORL  33.0         198.12   \n","6           6  Gheorghe Muresan               WAS  26.0         231.14   \n","7           7         Glen Rice               CHH  30.0         203.20   \n","8           8    Glenn Robinson               MIL  24.0         200.66   \n","9           9        Grant Hill               DET  24.0         203.20   \n","\n","   player_weight                college country draft_year draft_round  ...  \\\n","0      94.800728        Louisiana State     USA       1996           2  ...   \n","1      86.182480  Northwestern Oklahoma     USA       1994           2  ...   \n","2     103.418976         North Carolina     USA       1993           1  ...   \n","3     102.058200          Florida State     USA       1989           1  ...   \n","4     119.748288                   UCLA     USA       1995           1  ...   \n","5     102.058200  Tennessee-Chattanooga     USA       1985           2  ...   \n","6     137.438376                    NaN     USA       1993           2  ...   \n","7      99.790240               Michigan     USA       1989           1  ...   \n","8     106.594120                 Purdue     USA       1994           1  ...   \n","9     102.058200                   Duke     USA       1994           1  ...   \n","\n","    pts  reb  ast  net_rating  oreb_pct  dreb_pct  usg_pct  ts_pct  ast_pct  \\\n","0   3.9  1.5  2.4         0.3     0.042     0.071    0.169   0.487    0.248   \n","1   3.8  1.3  0.3         8.9     0.030     0.111    0.174   0.497    0.043   \n","2   8.3  6.4  1.9        -8.2     0.106     0.185    0.175   0.512    0.125   \n","3  10.2  2.8  1.7        -2.7     0.027     0.111    0.206   0.527    0.125   \n","4   2.8  1.7  0.3       -14.1     0.102     0.169    0.195   0.500    0.064   \n","5  10.6  2.2  2.2        -5.8     0.031     0.064    0.203   0.503    0.143   \n","6  10.6  6.6  0.4         6.9     0.098     0.217    0.185   0.618    0.024   \n","7  26.8  4.0  2.0         3.2     0.025     0.087    0.272   0.605    0.088   \n","8  21.1  6.3  3.1        -2.9     0.051     0.144    0.278   0.528    0.146   \n","9  21.4  9.0  7.3         6.9     0.049     0.232    0.283   0.556    0.356   \n","\n","    season  \n","0  1996-97  \n","1  1996-97  \n","2  1996-97  \n","3  1996-97  \n","4  1996-97  \n","5  1996-97  \n","6  1996-97  \n","7  1996-97  \n","8  1996-97  \n","9  1996-97  \n","\n","[10 rows x 22 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.head(10)"]},{"cell_type":"markdown","id":"65c293ff","metadata":{"papermill":{"duration":0.01096,"end_time":"2024-01-26T16:21:49.530734","exception":false,"start_time":"2024-01-26T16:21:49.519774","status":"completed"},"tags":[]},"source":["## Refining \n","\n","Since we do not need the Unnamed column here, we'll just remove it"]},{"cell_type":"code","execution_count":4,"id":"7dd8d13c","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.555407Z","iopub.status.busy":"2024-01-26T16:21:49.55501Z","iopub.status.idle":"2024-01-26T16:21:49.566941Z","shell.execute_reply":"2024-01-26T16:21:49.565825Z"},"papermill":{"duration":0.026833,"end_time":"2024-01-26T16:21:49.56933","exception":false,"start_time":"2024-01-26T16:21:49.542497","status":"completed"},"tags":[]},"outputs":[],"source":["nba_data.drop(columns='Unnamed: 0',inplace = True)"]},{"cell_type":"code","execution_count":5,"id":"bb9b8ecc","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.592749Z","iopub.status.busy":"2024-01-26T16:21:49.592348Z","iopub.status.idle":"2024-01-26T16:21:49.624934Z","shell.execute_reply":"2024-01-26T16:21:49.623846Z"},"papermill":{"duration":0.047757,"end_time":"2024-01-26T16:21:49.627881","exception":false,"start_time":"2024-01-26T16:21:49.580124","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>player_name</th>\n","      <th>team_abbreviation</th>\n","      <th>age</th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>college</th>\n","      <th>country</th>\n","      <th>draft_year</th>\n","      <th>draft_round</th>\n","      <th>draft_number</th>\n","      <th>...</th>\n","      <th>pts</th>\n","      <th>reb</th>\n","      <th>ast</th>\n","      <th>net_rating</th>\n","      <th>oreb_pct</th>\n","      <th>dreb_pct</th>\n","      <th>usg_pct</th>\n","      <th>ts_pct</th>\n","      <th>ast_pct</th>\n","      <th>season</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Randy Livingston</td>\n","      <td>HOU</td>\n","      <td>22.0</td>\n","      <td>193.04</td>\n","      <td>94.800728</td>\n","      <td>Louisiana State</td>\n","      <td>USA</td>\n","      <td>1996</td>\n","      <td>2</td>\n","      <td>42</td>\n","      <td>...</td>\n","      <td>3.9</td>\n","      <td>1.5</td>\n","      <td>2.4</td>\n","      <td>0.3</td>\n","      <td>0.042</td>\n","      <td>0.071</td>\n","      <td>0.169</td>\n","      <td>0.487</td>\n","      <td>0.248</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Gaylon Nickerson</td>\n","      <td>WAS</td>\n","      <td>28.0</td>\n","      <td>190.50</td>\n","      <td>86.182480</td>\n","      <td>Northwestern Oklahoma</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>2</td>\n","      <td>34</td>\n","      <td>...</td>\n","      <td>3.8</td>\n","      <td>1.3</td>\n","      <td>0.3</td>\n","      <td>8.9</td>\n","      <td>0.030</td>\n","      <td>0.111</td>\n","      <td>0.174</td>\n","      <td>0.497</td>\n","      <td>0.043</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>George Lynch</td>\n","      <td>VAN</td>\n","      <td>26.0</td>\n","      <td>203.20</td>\n","      <td>103.418976</td>\n","      <td>North Carolina</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>1</td>\n","      <td>12</td>\n","      <td>...</td>\n","      <td>8.3</td>\n","      <td>6.4</td>\n","      <td>1.9</td>\n","      <td>-8.2</td>\n","      <td>0.106</td>\n","      <td>0.185</td>\n","      <td>0.175</td>\n","      <td>0.512</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>George McCloud</td>\n","      <td>LAL</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Florida State</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>...</td>\n","      <td>10.2</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>-2.7</td>\n","      <td>0.027</td>\n","      <td>0.111</td>\n","      <td>0.206</td>\n","      <td>0.527</td>\n","      <td>0.125</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>George Zidek</td>\n","      <td>DEN</td>\n","      <td>23.0</td>\n","      <td>213.36</td>\n","      <td>119.748288</td>\n","      <td>UCLA</td>\n","      <td>USA</td>\n","      <td>1995</td>\n","      <td>1</td>\n","      <td>22</td>\n","      <td>...</td>\n","      <td>2.8</td>\n","      <td>1.7</td>\n","      <td>0.3</td>\n","      <td>-14.1</td>\n","      <td>0.102</td>\n","      <td>0.169</td>\n","      <td>0.195</td>\n","      <td>0.500</td>\n","      <td>0.064</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Gerald Wilkins</td>\n","      <td>ORL</td>\n","      <td>33.0</td>\n","      <td>198.12</td>\n","      <td>102.058200</td>\n","      <td>Tennessee-Chattanooga</td>\n","      <td>USA</td>\n","      <td>1985</td>\n","      <td>2</td>\n","      <td>47</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>2.2</td>\n","      <td>2.2</td>\n","      <td>-5.8</td>\n","      <td>0.031</td>\n","      <td>0.064</td>\n","      <td>0.203</td>\n","      <td>0.503</td>\n","      <td>0.143</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Gheorghe Muresan</td>\n","      <td>WAS</td>\n","      <td>26.0</td>\n","      <td>231.14</td>\n","      <td>137.438376</td>\n","      <td>NaN</td>\n","      <td>USA</td>\n","      <td>1993</td>\n","      <td>2</td>\n","      <td>30</td>\n","      <td>...</td>\n","      <td>10.6</td>\n","      <td>6.6</td>\n","      <td>0.4</td>\n","      <td>6.9</td>\n","      <td>0.098</td>\n","      <td>0.217</td>\n","      <td>0.185</td>\n","      <td>0.618</td>\n","      <td>0.024</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Glen Rice</td>\n","      <td>CHH</td>\n","      <td>30.0</td>\n","      <td>203.20</td>\n","      <td>99.790240</td>\n","      <td>Michigan</td>\n","      <td>USA</td>\n","      <td>1989</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>...</td>\n","      <td>26.8</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>3.2</td>\n","      <td>0.025</td>\n","      <td>0.087</td>\n","      <td>0.272</td>\n","      <td>0.605</td>\n","      <td>0.088</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Glenn Robinson</td>\n","      <td>MIL</td>\n","      <td>24.0</td>\n","      <td>200.66</td>\n","      <td>106.594120</td>\n","      <td>Purdue</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>21.1</td>\n","      <td>6.3</td>\n","      <td>3.1</td>\n","      <td>-2.9</td>\n","      <td>0.051</td>\n","      <td>0.144</td>\n","      <td>0.278</td>\n","      <td>0.528</td>\n","      <td>0.146</td>\n","      <td>1996-97</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Grant Hill</td>\n","      <td>DET</td>\n","      <td>24.0</td>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>Duke</td>\n","      <td>USA</td>\n","      <td>1994</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>...</td>\n","      <td>21.4</td>\n","      <td>9.0</td>\n","      <td>7.3</td>\n","      <td>6.9</td>\n","      <td>0.049</td>\n","      <td>0.232</td>\n","      <td>0.283</td>\n","      <td>0.556</td>\n","      <td>0.356</td>\n","      <td>1996-97</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10 rows × 21 columns</p>\n","</div>"],"text/plain":["        player_name team_abbreviation   age  player_height  player_weight  \\\n","0  Randy Livingston               HOU  22.0         193.04      94.800728   \n","1  Gaylon Nickerson               WAS  28.0         190.50      86.182480   \n","2      George Lynch               VAN  26.0         203.20     103.418976   \n","3    George McCloud               LAL  30.0         203.20     102.058200   \n","4      George Zidek               DEN  23.0         213.36     119.748288   \n","5    Gerald Wilkins               ORL  33.0         198.12     102.058200   \n","6  Gheorghe Muresan               WAS  26.0         231.14     137.438376   \n","7         Glen Rice               CHH  30.0         203.20      99.790240   \n","8    Glenn Robinson               MIL  24.0         200.66     106.594120   \n","9        Grant Hill               DET  24.0         203.20     102.058200   \n","\n","                 college country draft_year draft_round draft_number  ...  \\\n","0        Louisiana State     USA       1996           2           42  ...   \n","1  Northwestern Oklahoma     USA       1994           2           34  ...   \n","2         North Carolina     USA       1993           1           12  ...   \n","3          Florida State     USA       1989           1            7  ...   \n","4                   UCLA     USA       1995           1           22  ...   \n","5  Tennessee-Chattanooga     USA       1985           2           47  ...   \n","6                    NaN     USA       1993           2           30  ...   \n","7               Michigan     USA       1989           1            4  ...   \n","8                 Purdue     USA       1994           1            1  ...   \n","9                   Duke     USA       1994           1            3  ...   \n","\n","    pts  reb  ast  net_rating  oreb_pct  dreb_pct  usg_pct  ts_pct  ast_pct  \\\n","0   3.9  1.5  2.4         0.3     0.042     0.071    0.169   0.487    0.248   \n","1   3.8  1.3  0.3         8.9     0.030     0.111    0.174   0.497    0.043   \n","2   8.3  6.4  1.9        -8.2     0.106     0.185    0.175   0.512    0.125   \n","3  10.2  2.8  1.7        -2.7     0.027     0.111    0.206   0.527    0.125   \n","4   2.8  1.7  0.3       -14.1     0.102     0.169    0.195   0.500    0.064   \n","5  10.6  2.2  2.2        -5.8     0.031     0.064    0.203   0.503    0.143   \n","6  10.6  6.6  0.4         6.9     0.098     0.217    0.185   0.618    0.024   \n","7  26.8  4.0  2.0         3.2     0.025     0.087    0.272   0.605    0.088   \n","8  21.1  6.3  3.1        -2.9     0.051     0.144    0.278   0.528    0.146   \n","9  21.4  9.0  7.3         6.9     0.049     0.232    0.283   0.556    0.356   \n","\n","    season  \n","0  1996-97  \n","1  1996-97  \n","2  1996-97  \n","3  1996-97  \n","4  1996-97  \n","5  1996-97  \n","6  1996-97  \n","7  1996-97  \n","8  1996-97  \n","9  1996-97  \n","\n","[10 rows x 21 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.head(10)"]},{"cell_type":"code","execution_count":6,"id":"1d6f9a43","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.652957Z","iopub.status.busy":"2024-01-26T16:21:49.652293Z","iopub.status.idle":"2024-01-26T16:21:49.658982Z","shell.execute_reply":"2024-01-26T16:21:49.657941Z"},"papermill":{"duration":0.021702,"end_time":"2024-01-26T16:21:49.661226","exception":false,"start_time":"2024-01-26T16:21:49.639524","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(12844, 21)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.shape"]},{"cell_type":"code","execution_count":7,"id":"ad84d800","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.68648Z","iopub.status.busy":"2024-01-26T16:21:49.685739Z","iopub.status.idle":"2024-01-26T16:21:49.699598Z","shell.execute_reply":"2024-01-26T16:21:49.698633Z"},"papermill":{"duration":0.029121,"end_time":"2024-01-26T16:21:49.701869","exception":false,"start_time":"2024-01-26T16:21:49.672748","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["player_name             0\n","team_abbreviation       0\n","age                     0\n","player_height           0\n","player_weight           0\n","college              1854\n","country                 0\n","draft_year              0\n","draft_round             0\n","draft_number            0\n","gp                      0\n","pts                     0\n","reb                     0\n","ast                     0\n","net_rating              0\n","oreb_pct                0\n","dreb_pct                0\n","usg_pct                 0\n","ts_pct                  0\n","ast_pct                 0\n","season                  0\n","dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.isnull().sum()"]},{"cell_type":"markdown","id":"4d922c5a","metadata":{"papermill":{"duration":0.012745,"end_time":"2024-01-26T16:21:49.790132","exception":false,"start_time":"2024-01-26T16:21:49.777387","status":"completed"},"tags":[]},"source":["### Exploration\n","\n","The **nba_data.describe()** command is our trusty assistant, delivering a concise summary of the key numerical attributes within our dataset. It's like a stat sheet for statisticians, revealing the average heights, weights, defensive rebounding percentages, and other metrics that will guide our analysis.\n","\n"," By studying these descriptive statistics, we'll gain a deeper understanding of the players we're analyzing. We'll uncover the typical ranges for each variable, identify any potential outliers that might skew our results, and start to form initial hypotheses about the relationships we expect to find."]},{"cell_type":"code","execution_count":8,"id":"ae4c6872","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.816051Z","iopub.status.busy":"2024-01-26T16:21:49.815677Z","iopub.status.idle":"2024-01-26T16:21:49.873439Z","shell.execute_reply":"2024-01-26T16:21:49.872379Z"},"papermill":{"duration":0.074179,"end_time":"2024-01-26T16:21:49.876359","exception":false,"start_time":"2024-01-26T16:21:49.80218","status":"completed"},"tags":[]},"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>age</th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>gp</th>\n","      <th>pts</th>\n","      <th>reb</th>\n","      <th>ast</th>\n","      <th>net_rating</th>\n","      <th>oreb_pct</th>\n","      <th>dreb_pct</th>\n","      <th>usg_pct</th>\n","      <th>ts_pct</th>\n","      <th>ast_pct</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","      <td>12844.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>27.045313</td>\n","      <td>200.555097</td>\n","      <td>100.263279</td>\n","      <td>51.154158</td>\n","      <td>8.212582</td>\n","      <td>3.558486</td>\n","      <td>1.824681</td>\n","      <td>-2.226339</td>\n","      <td>0.054073</td>\n","      <td>0.140646</td>\n","      <td>0.184641</td>\n","      <td>0.513138</td>\n","      <td>0.131595</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4.339211</td>\n","      <td>9.111090</td>\n","      <td>12.426628</td>\n","      <td>25.084904</td>\n","      <td>6.016573</td>\n","      <td>2.477885</td>\n","      <td>1.800840</td>\n","      <td>12.665124</td>\n","      <td>0.043335</td>\n","      <td>0.062513</td>\n","      <td>0.053545</td>\n","      <td>0.101724</td>\n","      <td>0.094172</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>18.000000</td>\n","      <td>160.020000</td>\n","      <td>60.327736</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>-250.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>24.000000</td>\n","      <td>193.040000</td>\n","      <td>90.718400</td>\n","      <td>31.000000</td>\n","      <td>3.600000</td>\n","      <td>1.800000</td>\n","      <td>0.600000</td>\n","      <td>-6.400000</td>\n","      <td>0.021000</td>\n","      <td>0.096000</td>\n","      <td>0.149000</td>\n","      <td>0.482000</td>\n","      <td>0.066000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>26.000000</td>\n","      <td>200.660000</td>\n","      <td>99.790240</td>\n","      <td>57.000000</td>\n","      <td>6.700000</td>\n","      <td>3.000000</td>\n","      <td>1.200000</td>\n","      <td>-1.300000</td>\n","      <td>0.040000</td>\n","      <td>0.130500</td>\n","      <td>0.181000</td>\n","      <td>0.525000</td>\n","      <td>0.103000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>30.000000</td>\n","      <td>208.280000</td>\n","      <td>108.862080</td>\n","      <td>73.000000</td>\n","      <td>11.500000</td>\n","      <td>4.700000</td>\n","      <td>2.400000</td>\n","      <td>3.200000</td>\n","      <td>0.083000</td>\n","      <td>0.179000</td>\n","      <td>0.217000</td>\n","      <td>0.563000</td>\n","      <td>0.179000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>44.000000</td>\n","      <td>231.140000</td>\n","      <td>163.293120</td>\n","      <td>85.000000</td>\n","      <td>36.100000</td>\n","      <td>16.300000</td>\n","      <td>11.700000</td>\n","      <td>300.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.500000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                age  player_height  player_weight            gp           pts  \\\n","count  12844.000000   12844.000000   12844.000000  12844.000000  12844.000000   \n","mean      27.045313     200.555097     100.263279     51.154158      8.212582   \n","std        4.339211       9.111090      12.426628     25.084904      6.016573   \n","min       18.000000     160.020000      60.327736      1.000000      0.000000   \n","25%       24.000000     193.040000      90.718400     31.000000      3.600000   \n","50%       26.000000     200.660000      99.790240     57.000000      6.700000   \n","75%       30.000000     208.280000     108.862080     73.000000     11.500000   \n","max       44.000000     231.140000     163.293120     85.000000     36.100000   \n","\n","                reb           ast    net_rating      oreb_pct      dreb_pct  \\\n","count  12844.000000  12844.000000  12844.000000  12844.000000  12844.000000   \n","mean       3.558486      1.824681     -2.226339      0.054073      0.140646   \n","std        2.477885      1.800840     12.665124      0.043335      0.062513   \n","min        0.000000      0.000000   -250.000000      0.000000      0.000000   \n","25%        1.800000      0.600000     -6.400000      0.021000      0.096000   \n","50%        3.000000      1.200000     -1.300000      0.040000      0.130500   \n","75%        4.700000      2.400000      3.200000      0.083000      0.179000   \n","max       16.300000     11.700000    300.000000      1.000000      1.000000   \n","\n","            usg_pct        ts_pct       ast_pct  \n","count  12844.000000  12844.000000  12844.000000  \n","mean       0.184641      0.513138      0.131595  \n","std        0.053545      0.101724      0.094172  \n","min        0.000000      0.000000      0.000000  \n","25%        0.149000      0.482000      0.066000  \n","50%        0.181000      0.525000      0.103000  \n","75%        0.217000      0.563000      0.179000  \n","max        1.000000      1.500000      1.000000  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.describe()"]},{"cell_type":"markdown","id":"50317e1b","metadata":{"papermill":{"duration":0.011933,"end_time":"2024-01-26T16:21:49.901572","exception":false,"start_time":"2024-01-26T16:21:49.889639","status":"completed"},"tags":[]},"source":["### Plotting some statistics"]},{"cell_type":"code","execution_count":9,"id":"46b2e4a2","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:49.928092Z","iopub.status.busy":"2024-01-26T16:21:49.927658Z","iopub.status.idle":"2024-01-26T16:21:50.256441Z","shell.execute_reply":"2024-01-26T16:21:50.255271Z"},"papermill":{"duration":0.345295,"end_time":"2024-01-26T16:21:50.258945","exception":false,"start_time":"2024-01-26T16:21:49.91365","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7812e3555960>,\n"," <matplotlib.lines.Line2D at 0x7812e35552d0>,\n"," <matplotlib.lines.Line2D at 0x7812e35568c0>,\n"," <matplotlib.lines.Line2D at 0x7812e3571db0>,\n"," <matplotlib.lines.Line2D at 0x7812e3571f30>,\n"," <matplotlib.lines.Line2D at 0x7812e3570c10>,\n"," <matplotlib.lines.Line2D at 0x7812e3571ff0>,\n"," <matplotlib.lines.Line2D at 0x7812e35720e0>,\n"," <matplotlib.lines.Line2D at 0x7812e35721d0>,\n"," <matplotlib.lines.Line2D at 0x7812e35722c0>,\n"," <matplotlib.lines.Line2D at 0x7812e3556500>,\n"," <matplotlib.lines.Line2D at 0x7812e35723b0>,\n"," <matplotlib.lines.Line2D at 0x7812e35724a0>]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(nba_data.describe())"]},{"cell_type":"markdown","id":"66ec521d","metadata":{"papermill":{"duration":0.012328,"end_time":"2024-01-26T16:21:50.283957","exception":false,"start_time":"2024-01-26T16:21:50.271629","status":"completed"},"tags":[]},"source":["### Understanding the relation between height and weight"]},{"cell_type":"code","execution_count":10,"id":"36095101","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:50.311438Z","iopub.status.busy":"2024-01-26T16:21:50.310993Z","iopub.status.idle":"2024-01-26T16:21:50.809404Z","shell.execute_reply":"2024-01-26T16:21:50.808198Z"},"papermill":{"duration":0.515407,"end_time":"2024-01-26T16:21:50.812344","exception":false,"start_time":"2024-01-26T16:21:50.296937","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x1500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(15,15))\n","ax = plt.axes()\n","\n","ax.scatter(nba_data.player_height, nba_data.player_weight,s=10,c='r')\n","\n","ax.set(xlabel='Player Height (cm)',\n","       ylabel='Player Weight (kg)',\n","       title='Player Height vs Weight');"]},{"cell_type":"code","execution_count":11,"id":"6d7e2e3e","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:50.842518Z","iopub.status.busy":"2024-01-26T16:21:50.841449Z","iopub.status.idle":"2024-01-26T16:21:51.198023Z","shell.execute_reply":"2024-01-26T16:21:51.196895Z"},"papermill":{"duration":0.374306,"end_time":"2024-01-26T16:21:51.200452","exception":false,"start_time":"2024-01-26T16:21:50.826146","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsAAAAK9CAYAAAAqk9rwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAABNPElEQVR4nO3deXxN1/7/8feRSQxJTEmkSBQ1Dy2upjVWriBVSh+teSwdpK2xpXpRemnN1dvS2ypa2mv4oS4tYm7Rga+55iJFghoSQcmwfn945FxHgohEwno9H4/zeHSvvc7an72cxtvO2vs4jDFGAAAAgCXy5HQBAAAAwL1EAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABpCthg8fLofDcU+O1bBhQzVs2NC5vXbtWjkcDs2fP/+eHL9r164KCQm5J8fKrISEBL344osKDAyUw+FQnz597nrMG+cd1zgcDg0fPjzT742MjMzaggA4EYABZNiMGTPkcDicr7x58yooKEjh4eGaPHmyLly4kCXHOXHihIYPH65t27ZlyXhZKTfXlhGjRo3SjBkz9Morr+irr75Sp06dbto3JCTE5c/b399f9erV08KFC+9hxdmrYcOGqlKlSrr7jhw5IofDoXHjxt3jqjJu48aNGj58uM6fP5/TpQD3FfecLgDA/WfEiBEqXbq0EhMTFRsbq7Vr16pPnz6aMGGCFi9erGrVqjn7vvPOOxo0aNAdjX/ixAm9++67CgkJUY0aNTL8vhUrVtzRcTLjVrV99tlnSklJyfYa7sbq1av1+OOPa9iwYRnqX6NGDfXv31/StXP/9NNP1bp1a02ZMkUvv/xydpZ637t8+bLc3bP3r9mNGzfq3XffVdeuXeXn55etxwIeJARgAHesWbNmqlWrlnN78ODBWr16tZ5++mk988wz2rNnj7y9vSVJ7u7u2R4CLl26pHz58snT0zNbj3M7Hh4eOXr8jDh16pQqVaqU4f4PPfSQOnbs6Nzu3LmzypYtq4kTJ94XATglJUVXr15V3rx57/mxc+KYADKGJRAAssRTTz2lf/zjHzp69KhmzZrlbE9vDXBUVJTq1q0rPz8/FShQQOXLl9fbb78t6dq63dq1a0uSunXr5vz1+4wZMyT971fWW7ZsUf369ZUvXz7ne2+2FjU5OVlvv/22AgMDlT9/fj3zzDP6448/XPqEhISoa9euad57/Zi3qy29NcAXL15U//79VbJkSXl5eal8+fIaN26cjDEu/VLXfC5atEhVqlSRl5eXKleurGXLlqU/4Tc4deqUevTooYCAAOXNm1fVq1fXzJkznftT10MfPnxYS5cuddZ+5MiRDI2fKjAwUBUrVtThw4dv2ufq1asaOnSoatasKV9fX+XPn1/16tXTmjVrnH2MMQoJCVHLli3TvP+vv/6Sr6+vXnrpJWfblStXNGzYMJUtW1ZeXl4qWbKk3nzzTV25csXlvanzOHv2bFWuXFleXl4ZnsOMOn/+vPr06eP8My1btqw++OCDNFf/01sDvHbtWtWqVUt58+ZVmTJl9Omnn95ynfytPg/Dhw/XwIEDJUmlS5fO9J8pYCOuAAPIMp06ddLbb7+tFStWqGfPnun22b17t55++mlVq1ZNI0aMkJeXlw4ePKgNGzZIkipWrKgRI0Zo6NCh6tWrl+rVqydJeuKJJ5xjnDlzRs2aNVPbtm3VsWNHBQQE3LKuf/7zn3I4HHrrrbd06tQpTZo0SWFhYdq2bZvzSnVGZKS26xlj9Mwzz2jNmjXq0aOHatSooeXLl2vgwIE6fvy4Jk6c6NL/xx9/1IIFC/Tqq6+qYMGCmjx5stq0aaPo6GgVKVLkpnVdvnxZDRs21MGDBxUZGanSpUtr3rx56tq1q86fP6833nhDFStW1FdffaW+ffuqRIkSzmUNxYoVy/D5S1JiYqL++OOPW9YTHx+vzz//XO3atVPPnj114cIFTZs2TeHh4frll19Uo0YNORwOdezYUWPGjNHZs2dVuHBh5/v/+9//Kj4+3nnlOSUlRc8884x+/PFH9erVSxUrVtTOnTs1ceJE7d+/X4sWLXI5/urVqzV37lxFRkaqaNGit70xMTk5WX/++Wea9nPnzqVpu3Tpkho0aKDjx4/rpZdeUqlSpbRx40YNHjxYMTExmjRp0k2Ps3XrVjVt2lTFixfXu+++q+TkZI0YMeKmfwa3+zy0bt1a+/fv1zfffKOJEyeqaNGiku78zxSwkgGADJo+fbqRZH799deb9vH19TWPPvqoc3vYsGHm+h81EydONJLM6dOnbzrGr7/+aiSZ6dOnp9nXoEEDI8lMnTo13X0NGjRwbq9Zs8ZIMg899JCJj493ts+dO9dIMh9++KGzLTg42HTp0uW2Y96qti5dupjg4GDn9qJFi4wk895777n0e+6554zD4TAHDx50tkkynp6eLm3bt283ksxHH32U5ljXmzRpkpFkZs2a5Wy7evWqCQ0NNQUKFHA59+DgYBMREXHL8a7v26RJE3P69Glz+vRps337dtO2bVsjybz22mvOfjfOUVJSkrly5YrLWOfOnTMBAQGme/fuzrZ9+/YZSWbKlCkufZ955hkTEhJiUlJSjDHGfPXVVyZPnjzmhx9+cOk3depUI8ls2LDB2SbJ5MmTx+zevTtD55j6ebrVa+zYsc7+I0eONPnz5zf79+93GWfQoEHGzc3NREdHu9QybNgw53aLFi1Mvnz5zPHjx51tBw4cMO7u7ubGv44z+nkYO3askWQOHz6cofMFcA1LIABkqQIFCtzyaRCpN+p8++23mb5hzMvLS926dctw/86dO6tgwYLO7eeee07FixfXd999l6njZ9R3330nNzc3vf766y7t/fv3lzFG33//vUt7WFiYypQp49yuVq2afHx89Pvvv9/2OIGBgWrXrp2zzcPDQ6+//roSEhK0bt26TJ/DihUrVKxYMRUrVkzVq1fXvHnz1KlTJ33wwQc3fY+bm5tzPXZKSorOnj2rpKQk1apVS//3f//n7PfII4+oTp06mj17trPt7Nmz+v7779WhQwfnsoB58+apYsWKqlChgv7880/n66mnnpIkl6UVktSgQYM7WuccEhKiqKioNK/rl/KkmjdvnurVq6dChQq51BIWFqbk5GStX78+3WMkJydr5cqVatWqlYKCgpztZcuWVbNmzdJ9T2Y/DwBujyUQALJUQkKC/P39b7r/hRde0Oeff64XX3xRgwYNUuPGjdW6dWs999xzypMnY/8mf+ihh+7ohrdy5cq5bDscDpUtWzbb10oePXpUQUFBLuFburaUInX/9UqVKpVmjEKFCqX7q/gbj1OuXLk083ez49yJOnXq6L333pPD4VC+fPlUsWLFDD1tYObMmRo/frz27t2rxMREZ3vp0qVd+nXu3FmRkZE6evSogoODNW/ePCUmJro8nu3AgQPas2fPTX+1f+rUKZftG49xO/nz51dYWFia9vQ+HwcOHNCOHTsyXMv17ZcvX1bZsmXT7EuvTcr85wHA7RGAAWSZY8eOKS4u7qZ/oUuSt7e31q9frzVr1mjp0qVatmyZ5syZo6eeekorVqyQm5vbbY9zJ+t2M+pmNyElJydnqKascLPjmBtumLuXihYtmm44vJVZs2apa9euatWqlQYOHCh/f3+5ublp9OjROnTokEvftm3bqm/fvpo9e7befvttzZo1S7Vq1VL58uWdfVJSUlS1alVNmDAh3eOVLFnSZTs7Ph/X1/L3v/9db775Zrr7H3nkkSw7Vm78PAAPCgIwgCzz1VdfSZLCw8Nv2S9Pnjxq3LixGjdurAkTJmjUqFEaMmSI1qxZo7CwsCz/5rgDBw64bBtjdPDgQZfnFRcqVCjdLxM4evSoHn74Yef2ndQWHByslStX6sKFCy5Xgffu3evcnxWCg4O1Y8cOpaSkuFwFzurjZNT8+fP18MMPa8GCBS7zld6zhwsXLqyIiAjNnj1bHTp00IYNG9LcSFamTBlt375djRs3vmffKngzZcqUUUJCwh3/o8Df31958+bVwYMH0+xLry2jcno+gPsVa4ABZInVq1dr5MiRKl26tDp06HDTfmfPnk3TlvqFEqmPtMqfP78kZdm3W3355Zcu65Lnz5+vmJgYl7WXZcqU0U8//aSrV68625YsWZLmcWl3Ulvz5s2VnJysf/3rXy7tEydOlMPhuOnazzvVvHlzxcbGas6cOc62pKQkffTRRypQoIAaNGiQJcfJqNQrl9dfqfz555+1adOmdPt36tRJv/32mwYOHCg3Nze1bdvWZf/zzz+v48eP67PPPkvz3suXL+vixYtZWP2tPf/889q0aZOWL1+eZt/58+eVlJSU7vvc3NwUFhamRYsW6cSJE872gwcPplkLfiey+v8VwBZcAQZwx77//nvt3btXSUlJOnnypFavXq2oqCgFBwdr8eLFt/wCgBEjRmj9+vWKiIhQcHCwTp06pU8++UQlSpRQ3bp1JV0Lo35+fpo6daoKFiyo/Pnzq06dOne8tjNV4cKFVbduXXXr1k0nT57UpEmTVLZsWZdHtb344ouaP3++mjZtqueff16HDh3SrFmzXG5CutPaWrRooUaNGmnIkCE6cuSIqlevrhUrVujbb79Vnz590oydWb169dKnn36qrl27asuWLQoJCdH8+fOdV1NvXIOc3Z5++mktWLBAzz77rCIiInT48GFNnTpVlSpVUkJCQpr+ERERKlKkiObNm6dmzZqlWUPeqVMnzZ07Vy+//LLWrFmjJ598UsnJydq7d6/mzp2r5cuXu3wxS3YaOHCgFi9erKefflpdu3ZVzZo1dfHiRe3cuVPz58/XkSNHnI8ju9Hw4cO1YsUKPfnkk3rllVec/ziqUqVKpr9au2bNmpKkIUOGqG3btvLw8FCLFi2cwRjATeToMygA3FdSH4OW+vL09DSBgYHm73//u/nwww9dHreV6sbHoK1atcq0bNnSBAUFGU9PTxMUFGTatWuX5rFS3377ralUqZLzEVGpjx1r0KCBqVy5crr13ewxaN98840ZPHiw8ff3N97e3iYiIsIcPXo0zfvHjx9vHnroIePl5WWefPJJs3nz5jRj3qq2Gx+DZowxFy5cMH379jVBQUHGw8PDlCtXzowdO9b5iK9Ukkzv3r3T1HSzx7Pd6OTJk6Zbt26maNGixtPT01StWjXdR7Xd6WPQMtL3xjlKSUkxo0aNMsHBwcbLy8s8+uijZsmSJenOT6pXX33VSDJff/11uvuvXr1qPvjgA1O5cmXj5eVlChUqZGrWrGneffddExcX5+x3s3m8Ve03+zwdPnw4zWPQjLn2Zzp48GBTtmxZ4+npaYoWLWqeeOIJM27cOHP16lWXWq5/DJox1z7/jz76qPH09DRlypQxn3/+uenfv7/JmzevS787+TyMHDnSPPTQQyZPnjw8Eg3IIIcxrKYHAOSsvn37atq0aYqNjVW+fPlyupx7qlWrVtq9e3eateoAsg9rgAEAOeqvv/7SrFmz1KZNmwc+/F6+fNll+8CBA/ruu+/S/QpvANmHNcAAgBxx6tQprVy5UvPnz9eZM2f0xhtv5HRJ2e7hhx9W165d9fDDD+vo0aOaMmWKPD09b/pYNQDZgwAMAMgRv/32mzp06CB/f39NnjzZ+TSQB1nTpk31zTffKDY2Vl5eXgoNDdWoUaPSfFkLgOzFGmAAAABYhTXAAAAAsAoBGAAAAFZhDXAGpKSk6MSJEypYsCBfOwkAAJALGWN04cIFBQUFuXwtfHoIwBlw4sQJlSxZMqfLAAAAwG388ccfKlGixC37EIAzIPVrRP/44w/5+PjkcDUAAAC4UXx8vEqWLJmhr38nAGdA6rIHHx8fAjAAAEAulpHlqtwEBwAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAV95wuAACA+03IoKXZNvaR9yOybWwA13AFGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFbJ0QA8evRo1a5dWwULFpS/v79atWqlffv2ufRp2LChHA6Hy+vll1926RMdHa2IiAjly5dP/v7+GjhwoJKSklz6rF27Vo899pi8vLxUtmxZzZgxI7tPDwAAALlQjgbgdevWqXfv3vrpp58UFRWlxMRENWnSRBcvXnTp17NnT8XExDhfY8aMce5LTk5WRESErl69qo0bN2rmzJmaMWOGhg4d6uxz+PBhRUREqFGjRtq2bZv69OmjF198UcuXL79n5woAAIDcwT0nD75s2TKX7RkzZsjf319btmxR/fr1ne358uVTYGBgumOsWLFCv/32m1auXKmAgADVqFFDI0eO1FtvvaXhw4fL09NTU6dOVenSpTV+/HhJUsWKFfXjjz9q4sSJCg8Pz74TBAAAQK6Tq9YAx8XFSZIKFy7s0j579mwVLVpUVapU0eDBg3Xp0iXnvk2bNqlq1aoKCAhwtoWHhys+Pl67d+929gkLC3MZMzw8XJs2bUq3jitXrig+Pt7lBQAAgAdDjl4Bvl5KSor69OmjJ598UlWqVHG2t2/fXsHBwQoKCtKOHTv01ltvad++fVqwYIEkKTY21iX8SnJux8bG3rJPfHy8Ll++LG9vb5d9o0eP1rvvvpvl5wgAAICcl2sCcO/evbVr1y79+OOPLu29evVy/nfVqlVVvHhxNW7cWIcOHVKZMmWypZbBgwerX79+zu34+HiVLFkyW44FAACAeytXLIGIjIzUkiVLtGbNGpUoUeKWfevUqSNJOnjwoCQpMDBQJ0+edOmTup26bvhmfXx8fNJc/ZUkLy8v+fj4uLwAAADwYMjRAGyMUWRkpBYuXKjVq1erdOnSt33Ptm3bJEnFixeXJIWGhmrnzp06deqUs09UVJR8fHxUqVIlZ59Vq1a5jBMVFaXQ0NAsOhMAAADcL3I0APfu3VuzZs3S119/rYIFCyo2NlaxsbG6fPmyJOnQoUMaOXKktmzZoiNHjmjx4sXq3Lmz6tevr2rVqkmSmjRpokqVKqlTp07avn27li9frnfeeUe9e/eWl5eXJOnll1/W77//rjfffFN79+7VJ598orlz56pv3745du4AAADIGTkagKdMmaK4uDg1bNhQxYsXd77mzJkjSfL09NTKlSvVpEkTVahQQf3791ebNm303//+1zmGm5ublixZIjc3N4WGhqpjx47q3LmzRowY4exTunRpLV26VFFRUapevbrGjx+vzz//nEegAQAAWMhhjDE5XURuFx8fL19fX8XFxbEeGACgkEFLs23sI+9HZNvYwIPsTvJarrgJDgAAALhXCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKySowF49OjRql27tgoWLCh/f3+1atVK+/btc+nz119/qXfv3ipSpIgKFCigNm3a6OTJky59oqOjFRERoXz58snf318DBw5UUlKSS5+1a9fqsccek5eXl8qWLasZM2Zk9+kBAAAgF8rRALxu3Tr17t1bP/30k6KiopSYmKgmTZro4sWLzj59+/bVf//7X82bN0/r1q3TiRMn1Lp1a+f+5ORkRURE6OrVq9q4caNmzpypGTNmaOjQoc4+hw8fVkREhBo1aqRt27apT58+evHFF7V8+fJ7er4AAADIeQ5jjMnpIlKdPn1a/v7+WrdunerXr6+4uDgVK1ZMX3/9tZ577jlJ0t69e1WxYkVt2rRJjz/+uL7//ns9/fTTOnHihAICAiRJU6dO1VtvvaXTp0/L09NTb731lpYuXapdu3Y5j9W2bVudP39ey5Ytu21d8fHx8vX1VVxcnHx8fLLn5AEA942QQUuzbewj70dk29jAg+xO8lquWgMcFxcnSSpcuLAkacuWLUpMTFRYWJizT4UKFVSqVClt2rRJkrRp0yZVrVrVGX4lKTw8XPHx8dq9e7ezz/VjpPZJHeNGV65cUXx8vMsLAAAAD4ZcE4BTUlLUp08fPfnkk6pSpYokKTY2Vp6envLz83PpGxAQoNjYWGef68Nv6v7UfbfqEx8fr8uXL6epZfTo0fL19XW+SpYsmSXnCAAAgJyXawJw7969tWvXLv3nP//J6VI0ePBgxcXFOV9//PFHTpcEAACALOKe0wVIUmRkpJYsWaL169erRIkSzvbAwEBdvXpV58+fd7kKfPLkSQUGBjr7/PLLLy7jpT4l4vo+Nz454uTJk/Lx8ZG3t3eaery8vOTl5ZUl5wYAAIDcJUevABtjFBkZqYULF2r16tUqXbq0y/6aNWvKw8NDq1atcrbt27dP0dHRCg0NlSSFhoZq586dOnXqlLNPVFSUfHx8VKlSJWef68dI7ZM6BgAAAOyRo1eAe/fura+//lrffvutChYs6Fyz6+vrK29vb/n6+qpHjx7q16+fChcuLB8fH7322msKDQ3V448/Lklq0qSJKlWqpE6dOmnMmDGKjY3VO++8o969ezuv4r788sv617/+pTfffFPdu3fX6tWrNXfuXC1dmn138QIAACB3ytErwFOmTFFcXJwaNmyo4sWLO19z5sxx9pk4caKefvpptWnTRvXr11dgYKAWLFjg3O/m5qYlS5bIzc1NoaGh6tixozp37qwRI0Y4+5QuXVpLly5VVFSUqlevrvHjx+vzzz9XeHj4PT1fAAAA5Lxc9Rzg3IrnAAMArsdzgIHc5759DjAAAACQ3QjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAq2QqAP/+++9ZXQcAAABwT2QqAJctW1aNGjXSrFmz9Ndff2V1TQAAAEC2yVQA/r//+z9Vq1ZN/fr1U2BgoF566SX98ssvWV0bAAAAkOUyFYBr1KihDz/8UCdOnNAXX3yhmJgY1a1bV1WqVNGECRN0+vTprK4TAAAAyBJ3dROcu7u7WrdurXnz5umDDz7QwYMHNWDAAJUsWVKdO3dWTExMVtUJAAAAZIm7CsCbN2/Wq6++quLFi2vChAkaMGCADh06pKioKJ04cUItW7bMqjoBAACALOGemTdNmDBB06dP1759+9S8eXN9+eWXat68ufLkuZanS5curRkzZigkJCQrawUAAADuWqYC8JQpU9S9e3d17dpVxYsXT7ePv7+/pk2bdlfFAQAAAFktUwH4wIEDt+3j6empLl26ZGZ4AAAAINtkag3w9OnTNW/evDTt8+bN08yZM++6KAAAACC7ZCoAjx49WkWLFk3T7u/vr1GjRt11UQAAAEB2yVQAjo6OVunSpdO0BwcHKzo6+q6LAgAAALJLpgKwv7+/duzYkaZ9+/btKlKkyF0XBQAAAGSXTAXgdu3a6fXXX9eaNWuUnJys5ORkrV69Wm+88Ybatm2b1TUCAAAAWSZTT4EYOXKkjhw5osaNG8vd/doQKSkp6ty5M2uAAQAAkKtlKgB7enpqzpw5GjlypLZv3y5vb29VrVpVwcHBWV0fAAAAkKUyFYBTPfLII3rkkUeyqhYAAAAg22VqDXBycrKmTZum9u3bKywsTE899ZTLK6PWr1+vFi1aKCgoSA6HQ4sWLXLZ37VrVzkcDpdX06ZNXfqcPXtWHTp0kI+Pj/z8/NSjRw8lJCS49NmxY4fq1aunvHnzqmTJkhozZkxmThsAAAAPgExdAX7jjTc0Y8YMRUREqEqVKnI4HJk6+MWLF1W9enV1795drVu3TrdP06ZNNX36dOe2l5eXy/4OHTooJiZGUVFRSkxMVLdu3dSrVy99/fXXkqT4+Hg1adJEYWFhmjp1qnbu3Knu3bvLz89PvXr1ylTdAAAAuH9lKgD/5z//0dy5c9W8efO7OnizZs3UrFmzW/bx8vJSYGBguvv27NmjZcuW6ddff1WtWrUkSR999JGaN2+ucePGKSgoSLNnz9bVq1f1xRdfyNPTU5UrV9a2bds0YcIEAjAAAICFMrUEwtPTU2XLls3qWtK1du1a+fv7q3z58nrllVd05swZ575NmzbJz8/PGX4lKSwsTHny5NHPP//s7FO/fn15eno6+4SHh2vfvn06d+5cuse8cuWK4uPjXV4AAAB4MGQqAPfv318ffvihjDFZXY+Lpk2b6ssvv9SqVav0wQcfaN26dWrWrJmSk5MlSbGxsfL393d5j7u7uwoXLqzY2Fhnn4CAAJc+qdupfW40evRo+fr6Ol8lS5bM6lMDAABADsnUEogff/xRa9as0ffff6/KlSvLw8PDZf+CBQuypLjrv1SjatWqqlatmsqUKaO1a9eqcePGWXKM9AwePFj9+vVzbsfHxxOCAQAAHhCZCsB+fn569tlns7qW23r44YdVtGhRHTx4UI0bN1ZgYKBOnTrl0icpKUlnz551rhsODAzUyZMnXfqkbt9sbbGXl1eam+0AAADwYMhUAL7+qQz30rFjx3TmzBkVL15ckhQaGqrz589ry5YtqlmzpiRp9erVSklJUZ06dZx9hgwZosTEROeV6qioKJUvX16FChXKkfMAAABAzsnUGmDp2pXWlStX6tNPP9WFCxckSSdOnEjzDN5bSUhI0LZt27Rt2zZJ0uHDh7Vt2zZFR0crISFBAwcO1E8//aQjR45o1apVatmypcqWLavw8HBJUsWKFdW0aVP17NlTv/zyizZs2KDIyEi1bdtWQUFBkqT27dvL09NTPXr00O7duzVnzhx9+OGHLkscAAAAYI9MXQE+evSomjZtqujoaF25ckV///vfVbBgQX3wwQe6cuWKpk6dmqFxNm/erEaNGjm3U0Nply5dNGXKFO3YsUMzZ87U+fPnFRQUpCZNmmjkyJEuyxNmz56tyMhINW7cWHny5FGbNm00efJk535fX1+tWLFCvXv3Vs2aNVW0aFENHTqUR6ABAABYKtNfhFGrVi1t375dRYoUcbY/++yz6tmzZ4bHadiw4S2fJLF8+fLbjlG4cGHnl17cTLVq1fTDDz9kuC4AAAA8uDIVgH/44Qdt3LjR5dm6khQSEqLjx49nSWEAAABAdsjUGuCUlBTns3ivd+zYMRUsWPCuiwIAAACyS6YCcJMmTTRp0iTntsPhUEJCgoYNG3bXX48MAAAAZKdMLYEYP368wsPDValSJf31119q3769Dhw4oKJFi+qbb77J6hoBAACALJOpAFyiRAlt375d//nPf7Rjxw4lJCSoR48e6tChg7y9vbO6RgAAACDLZCoAS5K7u7s6duyYlbUAAAAA2S5TAfjLL7+85f7OnTtnqhgAAAAgu2X6OcDXS0xM1KVLl+Tp6al8+fIRgAEAAJBrZeopEOfOnXN5JSQkaN++fapbty43wQEAACBXy1QATk+5cuX0/vvvp7k6DAAAAOQmWRaApWs3xp04cSIrhwQAAACyVKbWAC9evNhl2xijmJgY/etf/9KTTz6ZJYUBAAAA2SFTAbhVq1Yu2w6HQ8WKFdNTTz2l8ePHZ0VdAAAAQLbIVABOSUnJ6joAAACAeyJL1wADAAAAuV2mrgD369cvw30nTJiQmUMAAAAA2SJTAXjr1q3aunWrEhMTVb58eUnS/v375ebmpscee8zZz+FwZE2VAAAAQBbJVABu0aKFChYsqJkzZ6pQoUKSrn05Rrdu3VSvXj31798/S4sEAAAAskqm1gCPHz9eo0ePdoZfSSpUqJDee+89ngIBAACAXC1TATg+Pl6nT59O03769GlduHDhrosCAAAAskumAvCzzz6rbt26acGCBTp27JiOHTum//f//p969Oih1q1bZ3WNAAAAQJbJ1BrgqVOnasCAAWrfvr0SExOvDeTurh49emjs2LFZWiAAAACQlTIVgPPly6dPPvlEY8eO1aFDhyRJZcqUUf78+bO0OAAAACCr3dUXYcTExCgmJkblypVT/vz5ZYzJqroAAACAbJGpAHzmzBk1btxYjzzyiJo3b66YmBhJUo8ePXgEGgAAAHK1TAXgvn37ysPDQ9HR0cqXL5+z/YUXXtCyZcuyrDgAAAAgq2VqDfCKFSu0fPlylShRwqW9XLlyOnr0aJYUBgAAAGSHTF0BvnjxosuV31Rnz56Vl5fXXRcFAAAAZJdMBeB69erpyy+/dG47HA6lpKRozJgxatSoUZYVBwAAAGS1TC2BGDNmjBo3bqzNmzfr6tWrevPNN7V7926dPXtWGzZsyOoaAQAAgCyTqSvAVapU0f79+1W3bl21bNlSFy9eVOvWrbV161aVKVMmq2sEAAAAsswdXwFOTExU06ZNNXXqVA0ZMiQ7agIAAACyzR1fAfbw8NCOHTuyoxYAAAAg22VqCUTHjh01bdq0rK4FAAAAyHaZugkuKSlJX3zxhVauXKmaNWsqf/78LvsnTJiQJcUBAAAAWe2OAvDvv/+ukJAQ7dq1S4899pgkaf/+/S59HA5H1lUHAAAAZLE7CsDlypVTTEyM1qxZI+naVx9PnjxZAQEB2VIcAAAAkNXuaA2wMcZl+/vvv9fFixeztCAAAAAgO2XqJrhUNwZiAAAAILe7owDscDjSrPFlzS8AAADuJ3e0BtgYo65du8rLy0uS9Ndff+nll19O8xSIBQsWZF2FAAAAQBa6owDcpUsXl+2OHTtmaTEAAPuEDFqabWMfeT8i28YGcP+6owA8ffr07KoDAAAAuCfu6iY4AAAA4H5DAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCp39BQIAADuJ9n5iDUA9y+uAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAq7jndAEAgPtDyKClOV0CAGQJrgADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBX3nC4AAAD8T8igpdky7pH3I7JlXOB+xBVgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACswk1wAPAAya4bqADgQcIVYAAAAFglRwPw+vXr1aJFCwUFBcnhcGjRokUu+40xGjp0qIoXLy5vb2+FhYXpwIEDLn3Onj2rDh06yMfHR35+furRo4cSEhJc+uzYsUP16tVT3rx5VbJkSY0ZMya7Tw0AAAC5VI4G4IsXL6p69er6+OOP090/ZswYTZ48WVOnTtXPP/+s/PnzKzw8XH/99ZezT4cOHbR7925FRUVpyZIlWr9+vXr16uXcHx8fryZNmig4OFhbtmzR2LFjNXz4cP373//O9vMDAABA7uMwxpicLkKSHA6HFi5cqFatWkm6dvU3KChI/fv314ABAyRJcXFxCggI0IwZM9S2bVvt2bNHlSpV0q+//qpatWpJkpYtW6bmzZvr2LFjCgoK0pQpUzRkyBDFxsbK09NTkjRo0CAtWrRIe/fuzVBt8fHx8vX1VVxcnHx8fLL+5AEgi7AGGDfDF2HgQXcneS3XrgE+fPiwYmNjFRYW5mzz9fVVnTp1tGnTJknSpk2b5Ofn5wy/khQWFqY8efLo559/dvapX7++M/xKUnh4uPbt26dz586le+wrV64oPj7e5QUAAIAHQ64NwLGxsZKkgIAAl/aAgADnvtjYWPn7+7vsd3d3V+HChV36pDfG9ce40ejRo+Xr6+t8lSxZ8u5PCAAAALlCrg3AOWnw4MGKi4tzvv7444+cLgkAAABZJNcG4MDAQEnSyZMnXdpPnjzp3BcYGKhTp0657E9KStLZs2dd+qQ3xvXHuJGXl5d8fHxcXgAAAHgw5NoAXLp0aQUGBmrVqlXOtvj4eP38888KDQ2VJIWGhur8+fPasmWLs8/q1auVkpKiOnXqOPusX79eiYmJzj5RUVEqX768ChUqdI/OBgAAALlFjgbghIQEbdu2Tdu2bZN07ca3bdu2KTo6Wg6HQ3369NF7772nxYsXa+fOnercubOCgoKcT4qoWLGimjZtqp49e+qXX37Rhg0bFBkZqbZt2yooKEiS1L59e3l6eqpHjx7avXu35syZow8//FD9+vXLobMGAABATsrRr0LevHmzGjVq5NxODaVdunTRjBkz9Oabb+rixYvq1auXzp8/r7p162rZsmXKmzev8z2zZ89WZGSkGjdurDx58qhNmzaaPHmyc7+vr69WrFih3r17q2bNmipatKiGDh3q8qxgAAAA2CPXPAc4N+M5wADuFzwHGDfDc4DxoHsgngMMAAAAZAcCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsIp7ThcAALlVyKCl2Tb2kfcjsm1sAMCtcQUYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKu453QBAOwQMmhptox75P2IbBkXAPDg4gowAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKjwFAgByQHY9FQMAcHtcAQYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArMIXYQAAYIHs/PKVI+9HZNvYQHbgCjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAVnHP6QIA4G6EDFqa0yUAAO4zXAEGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVcnUAHj58uBwOh8urQoUKzv1//fWXevfurSJFiqhAgQJq06aNTp486TJGdHS0IiIilC9fPvn7+2vgwIFKSkq616cCAACAXCLXfxVy5cqVtXLlSue2u/v/Su7bt6+WLl2qefPmydfXV5GRkWrdurU2bNggSUpOTlZERIQCAwO1ceNGxcTEqHPnzvLw8NCoUaPu+bkAAAAg5+X6AOzu7q7AwMA07XFxcZo2bZq+/vprPfXUU5Kk6dOnq2LFivrpp5/0+OOPa8WKFfrtt9+0cuVKBQQEqEaNGho5cqTeeustDR8+XJ6envf6dAAAAJDDcvUSCEk6cOCAgoKC9PDDD6tDhw6Kjo6WJG3ZskWJiYkKCwtz9q1QoYJKlSqlTZs2SZI2bdqkqlWrKiAgwNknPDxc8fHx2r17902PeeXKFcXHx7u8AAAA8GDI1QG4Tp06mjFjhpYtW6YpU6bo8OHDqlevni5cuKDY2Fh5enrKz8/P5T0BAQGKjY2VJMXGxrqE39T9qftuZvTo0fL19XW+SpYsmbUnBgAAgByTq5dANGvWzPnf1apVU506dRQcHKy5c+fK29s72447ePBg9evXz7kdHx9PCAYAAHhA5OorwDfy8/PTI488ooMHDyowMFBXr17V+fPnXfqcPHnSuWY4MDAwzVMhUrfTW1ecysvLSz4+Pi4vAAAAPBjuqwCckJCgQ4cOqXjx4qpZs6Y8PDy0atUq5/59+/YpOjpaoaGhkqTQ0FDt3LlTp06dcvaJioqSj4+PKlWqdM/rBwAAQM7L1UsgBgwYoBYtWig4OFgnTpzQsGHD5Obmpnbt2snX11c9evRQv379VLhwYfn4+Oi1115TaGioHn/8cUlSkyZNVKlSJXXq1EljxoxRbGys3nnnHfXu3VteXl45fHYAAADICbk6AB87dkzt2rXTmTNnVKxYMdWtW1c//fSTihUrJkmaOHGi8uTJozZt2ujKlSsKDw/XJ5984ny/m5ublixZoldeeUWhoaHKnz+/unTpohEjRuTUKQEAACCHOYwxJqeLyO3i4+Pl6+uruLg41gMDmRQyaGlOlwAgmxx5PyKnSwDuKK/dV2uAAQAAgLtFAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFQIwAAAArEIABgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABgFfecLgAAANzfQgYtzZZxj7wfkS3jAlwBBgAAgFUIwAAAALAKARgAAABWIQADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAMAAMAqBGAAAABYhQAMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwintOFwAg9wgZtDSnSwAAINtxBRgAAABWIQADAADAKgRgAAAAWIUADAAAAKtwExwAAMiVsvPG3CPvR2Tb2Mj9uAIMAAAAqxCAAQAAYBUCMAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAVuExaMB9KDsfDQQAwIOOK8AAAACwCgEYAAAAVmEJBAAAsA7fMmc3rgADAADAKgRgAAAAWIUADAAAAKuwBhjIJjyqDACA3IkADAAAYLnsumiTW28ItGoJxMcff6yQkBDlzZtXderU0S+//JLTJQEAAOAes+YK8Jw5c9SvXz9NnTpVderU0aRJkxQeHq59+/bJ398/p8u7p3j0CwAAsJk1AXjChAnq2bOnunXrJkmaOnWqli5dqi+++EKDBg3K4eoAAMCDgntAcj8rAvDVq1e1ZcsWDR482NmWJ08ehYWFadOmTWn6X7lyRVeuXHFux8XFSZLi4+Ozv9jrVBm2/J4eLyvc6znKzVKuXMrpEgAAyFH3MhekHssYc9u+VgTgP//8U8nJyQoICHBpDwgI0N69e9P0Hz16tN5999007SVLlsy2Gh8UvpNyugIAAJBb5EQuuHDhgnx9fW/Zx4oAfKcGDx6sfv36ObdTUlJ09uxZFSlSRA6HI9uPHx8fr5IlS+qPP/6Qj49Pth/vQcP8ZR5zl3nMXeYxd3eH+cs85i7zcuPcGWN04cIFBQUF3bavFQG4aNGicnNz08mTJ13aT548qcDAwDT9vby85OXl5dLm5+eXnSWmy8fHJ9d8qO5HzF/mMXeZx9xlHnN3d5i/zGPuMi+3zd3trvymsuIxaJ6enqpZs6ZWrVrlbEtJSdGqVasUGhqag5UBAADgXrPiCrAk9evXT126dFGtWrX0t7/9TZMmTdLFixedT4UAAACAHawJwC+88IJOnz6toUOHKjY2VjVq1NCyZcvS3BiXG3h5eWnYsGFplmEgY5i/zGPuMo+5yzzm7u4wf5nH3GXe/T53DpORZ0UAAAAADwgr1gADAAAAqQjAAAAAsAoBGAAAAFYhAAMAAMAqBOB7aP369WrRooWCgoLkcDi0aNGiNH327NmjZ555Rr6+vsqfP79q166t6Oho5/6//vpLvXv3VpEiRVSgQAG1adMmzRd8PIhuN3cOhyPd19ixY519zp49qw4dOsjHx0d+fn7q0aOHEhIS7vGZ3Hu3m7uEhARFRkaqRIkS8vb2VqVKlTR16lSXPnzu0p+7kydPqmvXrgoKClK+fPnUtGlTHThwwKWPrXM3evRo1a5dWwULFpS/v79atWqlffv2ufTJyNxER0crIiJC+fLlk7+/vwYOHKikpKR7eSr3XEbm7t///rcaNmwoHx8fORwOnT9/Ps04tv7Mu938nT17Vq+99prKly8vb29vlSpVSq+//rri4uJcxuGzl/5n76WXXlKZMmXk7e2tYsWKqWXLltq7d69Ln/th7gjA99DFixdVvXp1ffzxx+nuP3TokOrWrasKFSpo7dq12rFjh/7xj38ob968zj59+/bVf//7X82bN0/r1q3TiRMn1Lp163t1CjnmdnMXExPj8vriiy/kcDjUpk0bZ58OHTpo9+7dioqK0pIlS7R+/Xr16tXrXp1Cjrnd3PXr10/Lli3TrFmztGfPHvXp00eRkZFavHixsw+fu7RzZ4xRq1at9Pvvv+vbb7/V1q1bFRwcrLCwMF28eNHZz9a5W7dunXr37q2ffvpJUVFRSkxMVJMmTe5obpKTkxUREaGrV69q48aNmjlzpmbMmKGhQ4fmxCndMxmZu0uXLqlp06Z6++23bzqOrT/zbjd/J06c0IkTJzRu3Djt2rVLM2bM0LJly9SjRw/nGHz2bv7Zq1mzpqZPn649e/Zo+fLlMsaoSZMmSk5OlnQfzZ1BjpBkFi5c6NL2wgsvmI4dO970PefPnzceHh5m3rx5zrY9e/YYSWbTpk3ZVWquk97c3ahly5bmqaeecm7/9ttvRpL59ddfnW3ff/+9cTgc5vjx49lVaq6T3txVrlzZjBgxwqXtscceM0OGDDHG8LlLdePc7du3z0gyu3btcrYlJyebYsWKmc8++8wYw9xd79SpU0aSWbdunTEmY3Pz3XffmTx58pjY2FhnnylTphgfHx9z5cqVe3sCOejGubvemjVrjCRz7tw5l3Z+5v3PreYv1dy5c42np6dJTEw0xvDZS5WRudu+fbuRZA4ePGiMuX/mjivAuURKSoqWLl2qRx55ROHh4fL391edOnVcfuW6ZcsWJSYmKiwszNlWoUIFlSpVSps2bcqBqnOnkydPaunSpS7/mt+0aZP8/PxUq1YtZ1tYWJjy5Mmjn3/+OSfKzDWeeOIJLV68WMePH5cxRmvWrNH+/fvVpEkTSXzububKlSuS5PIbmjx58sjLy0s//vijJObueqm/Xi5cuLCkjM3Npk2bVLVqVZcvLAoPD1d8fLx27959D6vPWTfOXUbwM+9/MjJ/cXFx8vHxkbv7te8H47N3ze3m7uLFi5o+fbpKly6tkiVLSrp/5o4AnEucOnVKCQkJev/999W0aVOtWLFCzz77rFq3bq1169ZJkmJjY+Xp6Sk/Pz+X9wYEBCg2NjYHqs6dZs6cqYIFC7r8KjU2Nlb+/v4u/dzd3VW4cGHr5+6jjz5SpUqVVKJECXl6eqpp06b6+OOPVb9+fUl87m4mNawNHjxY586d09WrV/XBBx/o2LFjiomJkcTcpUpJSVGfPn305JNPqkqVKpIyNjexsbFpvq0zdduW+Utv7jKCn3nXZGT+/vzzT40cOdJleQifvVvP3SeffKICBQqoQIEC+v777xUVFSVPT09J98/cWfNVyLldSkqKJKlly5bq27evJKlGjRrauHGjpk6dqgYNGuRkefeVL774Qh06dHC5Moeb++ijj/TTTz9p8eLFCg4O1vr169W7d28FBQW5XJ2DKw8PDy1YsEA9evRQ4cKF5ebmprCwMDVr1kyGL9h00bt3b+3atct5ZRwZx9zdndvNX3x8vCIiIlSpUiUNHz783haXy91q7jp06KC///3viomJ0bhx4/T8889rw4YN99Xfu1wBziWKFi0qd3d3VapUyaW9YsWKzqdABAYG6urVq2nu9j158qQCAwPvVam52g8//KB9+/bpxRdfdGkPDAzUqVOnXNqSkpJ09uxZq+fu8uXLevvttzVhwgS1aNFC1apVU2RkpF544QWNGzdOEp+7W6lZs6a2bdum8+fPKyYmRsuWLdOZM2f08MMPS2LuJCkyMlJLlizRmjVrVKJECWd7RuYmMDAwzVMhUrdtmL+bzV1G8DPv9vN34cIFNW3aVAULFtTChQvl4eHh3Mdn79Zz5+vrq3Llyql+/fqaP3++9u7dq4ULF0q6f+aOAJxLeHp6qnbt2mkeN7J//34FBwdLuvaXrYeHh1atWuXcv2/fPkVHRys0NPSe1ptbTZs2TTVr1lT16tVd2kNDQ3X+/Hlt2bLF2bZ69WqlpKSoTp0697rMXCMxMVGJiYnKk8f1R4Gbm5vztxJ87m7P19dXxYoV04EDB7R582a1bNlSkt1zZ4xRZGSkFi5cqNWrV6t06dIu+zMyN6Ghodq5c6dLkIuKipKPj0+aiwUPktvNXUbY/DMvI/MXHx+vJk2ayNPTU4sXL05z5ZLPXsY/e8YYGWOc90XcN3OXc/ff2efChQtm69atZuvWrUaSmTBhgtm6das5evSoMcaYBQsWGA8PD/Pvf//bHDhwwHz00UfGzc3N/PDDD84xXn75ZVOqVCmzevVqs3nzZhMaGmpCQ0Nz6pTumdvNnTHGxMXFmXz58pkpU6akO0bTpk3No48+an7++Wfz448/mnLlypl27drdq1PIMbebuwYNGpjKlSubNWvWmN9//91Mnz7d5M2b13zyySfOMfjcpT93c+fONWvWrDGHDh0yixYtMsHBwaZ169YuY9g6d6+88orx9fU1a9euNTExMc7XpUuXnH1uNzdJSUmmSpUqpkmTJmbbtm1m2bJlplixYmbw4ME5cUr3TEbmLiYmxmzdutV89tlnRpJZv3692bp1qzlz5oyzj60/8243f3FxcaZOnTqmatWq5uDBgy59kpKSjDF89m42d4cOHTKjRo0ymzdvNkePHjUbNmwwLVq0MIULFzYnT540xtw/c0cAvodSH1dz46tLly7OPtOmTTNly5Y1efPmNdWrVzeLFi1yGePy5cvm1VdfNYUKFTL58uUzzz77rImJibnHZ3LvZWTuPv30U+Pt7W3Onz+f7hhnzpwx7dq1MwUKFDA+Pj6mW7du5sKFC/foDHLO7eYuJibGdO3a1QQFBZm8efOa8uXLm/Hjx5uUlBTnGHzu0p+7Dz/80JQoUcJ4eHiYUqVKmXfeeSfNY35snbv05k2SmT59urNPRubmyJEjplmzZsbb29sULVrU9O/f3/moqgdVRuZu2LBht+1j68+8283fzf6/lmQOHz7sHIfPXtq5O378uGnWrJnx9/c3Hh4epkSJEqZ9+/Zm7969LuPcD3PnMIa7NQAAAGAP1gADAADAKgRgAAAAWIUADAAAAKsQgAEAAGAVAjAAAACsQgAGAACAVQjAAAAAsAoBGAAAAFYhAAPATcyYMUN+fn45XUa2O3LkiBwOh7Zt25bh92T13Jw5c0b+/v46cuRIlo15oz///FP+/v46duxYth0DwP2BAAzAWl27dpXD4ZDD4ZCnp6fKli2rESNGKCkpKadLy5CQkBBNmjQpTfvw4cNVo0aNDI9TsmRJxcTEqEqVKllXnK7Nb6tWrTLU95///KdatmypkJCQLK3hekWLFlXnzp01bNiwbDsGgPsDARiA1Zo2baqYmBgdOHBA/fv31/DhwzV27NicLstFYmJito7v5uamwMBAubu7Z+txbubSpUuaNm2aevToke3H6tatm2bPnq2zZ89m+7EA5F4EYABW8/LyUmBgoIKDg/XKK68oLCxMixcvTrfvoUOH1LJlSwUEBKhAgQKqXbu2Vq5c6dw/YsSIdK+i1qhRQ//4xz+c259//rkqVqyovHnzqkKFCvrkk0+c+1KXI8yZM0cNGjRQ3rx5NXv27Ls+z4wc8/olEIsXL1a5cuWUN29eNWrUSDNnzpTD4dD58+ddxl2+fLkqVqyoAgUKOP8xIV27Cj1z5kx9++23zqvsa9euTbe27777Tl5eXnr88cdd2nfv3q2nn35aPj4+KliwoOrVq6dDhw5J+t/V5VGjRikgIEB+fn7Oq/cDBw5U4cKFVaJECU2fPt1lzMqVKysoKEgLFy7M5EwCeBAQgAHgOt7e3rp69Wq6+xISEtS8eXOtWrVKW7duVdOmTdWiRQtFR0dLkrp37649e/bo119/db5n69at2rFjh7p16yZJmj17toYOHap//vOf2rNnj0aNGqV//OMfmjlzpsuxBg0apDfeeEN79uxReHj4XZ1TRo+Z6vDhw3ruuefUqlUrbd++XS+99JKGDBmSpt+lS5c0btw4ffXVV1q/fr2io6M1YMAASdKAAQP0/PPPO0NxTEyMnnjiiXSP98MPP6hmzZoubcePH1f9+vXl5eWl1atXa8uWLerevbvL8pTVq1frxIkTWr9+vSZMmKBhw4bp6aefVqFChfTzzz/r5Zdf1ksvvZRmze/f/vY3/fDDD3c0hwAeMAYALNWlSxfTsmVLY4wxKSkpJioqynh5eZkBAwYYY4yZPn268fX1veUYlStXNh999JFzu1mzZuaVV15xbr/22mumYcOGzu0yZcqYr7/+2mWMkSNHmtDQUGOMMYcPHzaSzKRJk25bf3BwsPH09DT58+d3eXl4eJjq1avf8TG3bt1qjDHmrbfeMlWqVHHpP2TIECPJnDt3zhhzbW4kmYMHDzr7fPzxxyYgIMC5ff383krLli1N9+7dXdoGDx5sSpcuba5evZrue7p06WKCg4NNcnKys618+fKmXr16zu2kpCSTP39+880337i8t2/fvi5/JgDskzMLvgAgl1iyZIkKFCigxMREpaSkqH379ho+fHi6fRMSEjR8+HAtXbpUMTExSkpK0uXLl51XgCWpZ8+e6t69uyZMmKA8efLo66+/1sSJEyVJFy9e1KFDh9SjRw/17NnT+Z6kpCT5+vq6HKtWrVoZqn/gwIHq2rWrS9vkyZO1fv36Oz5mqn379ql27doubX/729/S9MuXL5/KlCnj3C5evLhOnTqVobqvd/nyZeXNm9elbdu2bapXr548PDxu+r7KlSsrT57//SIzICDAZQmKm5ubihQpkqYmb29vXbp06Y7rBPDgIAADsFqjRo00ZcoUeXp6Kigo6JY3gg0YMEBRUVEaN26cypYtK29vbz333HMuSyZatGghLy8vLVy4UJ6enkpMTNRzzz0n6VqAlqTPPvtMderUcRnbzc3NZTt//vwZqr9o0aIqW7asS1vhwoWd/30nx7xTN4ZTh8MhY8wdj1O0aFGdO3fOpc3b2ztTx0+vLSUlxaXt7NmzKlas2B3XCeDBQQAGYLX8+fOnCZA3s2HDBnXt2lXPPvuspGvh8sbn1rq7u6tLly6aPn26PD091bZtW2eYCwgIUFBQkH7//Xd16NAhS8/jZjJzzPLly+u7775zabt+XXNGeXp6Kjk5+bb9Hn30Uc2aNculrVq1apo5c6YSExNveRU4M3bt2qWGDRtm6ZgA7i/cBAcAGVSuXDktWLBA27Zt0/bt29W+ffs0Vxcl6cUXX9Tq1au1bNkyde/e3WXfu+++q9GjR2vy5Mnav3+/du7cqenTp2vChAnZVvedHvOll17S3r179dZbb2n//v2aO3euZsyYIenaFdWMCgkJ0Y4dO7Rv3z79+eefN32cW3h4uHbv3u1yFTgyMlLx8fFq27atNm/erAMHDuirr77Svn37Mn7i6bh06ZK2bNmiJk2a3NU4AO5vBGAAyKAJEyaoUKFCeuKJJ9SiRQuFh4frscceS9OvXLlyeuKJJ1ShQoU0yw5efPFFff7555o+fbqqVq2qBg0aaMaMGSpdunS21X2nxyxdurTmz5+vBQsWqFq1apoyZYrzKRBeXl4ZPm7Pnj1Vvnx51apVS8WKFdOGDRvS7Ve1alU99thjmjt3rrOtSJEiWr16tRISEtSgQQPVrFlTn3322V1fDf72229VqlQp1atX767GAXB/c5jMLNgCANyUMUblypXTq6++qn79+uV0OVnin//8p6ZOnao//vgjW8ZfunSpBg4cqF27drnc2JbVHn/8cb3++utq3759th0DQO7HGmAAyEKnT5/Wf/7zH8XGxjqf/Xs/+uSTT1S7dm0VKVJEGzZs0NixYxUZGZltx4uIiNCBAwd0/PhxlSxZMluO8eeff6p169Zq165dtowP4P7BFWAAyEIOh0NFixbVhx9+eF9fZezbt6/mzJmjs2fPqlSpUurUqZMGDx6cY1+XDABZiQAMAAAAq3ATHAAAAKxCAAYAAIBVCMAAAACwCgEYAAAAViEAAwAAwCoEYAAAAFiFAAwAAACrEIABAABglf8PUCCwjJPEVgYAAAAASUVORK5CYII=","text/plain":["<Figure size 800x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8,8))\n","ax = plt.axes()\n","ax.hist(nba_data.player_height, bins=25);\n","\n","ax.set(xlabel='Player Height (cm)', \n","       ylabel='Frequency',\n","       title='Distribution of Player Height');"]},{"cell_type":"markdown","id":"caa4d17c","metadata":{"papermill":{"duration":0.0136,"end_time":"2024-01-26T16:21:51.227953","exception":false,"start_time":"2024-01-26T16:21:51.214353","status":"completed"},"tags":[]},"source":["## Fact-Check\n","\n","Here we'll actually check whether **Heighted Players** actually dominate or not"]},{"cell_type":"code","execution_count":12,"id":"cd089914","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:51.258469Z","iopub.status.busy":"2024-01-26T16:21:51.258061Z","iopub.status.idle":"2024-01-26T16:21:51.807466Z","shell.execute_reply":"2024-01-26T16:21:51.806301Z"},"papermill":{"duration":0.567417,"end_time":"2024-01-26T16:21:51.810115","exception":false,"start_time":"2024-01-26T16:21:51.242698","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1000x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["heatmap_data = nba_data[['age','player_height','player_weight','gp','pts','reb','ast','net_rating','oreb_pct','dreb_pct','usg_pct',\n","                        'ts_pct','ast_pct']]\n","fig_dims = (10, 10)\n","fig, ax = plt.subplots(figsize=fig_dims)\n","sea.heatmap(heatmap_data.corr())"]},{"cell_type":"code","execution_count":13,"id":"0458455a","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:51.844178Z","iopub.status.busy":"2024-01-26T16:21:51.843561Z","iopub.status.idle":"2024-01-26T16:21:51.87245Z","shell.execute_reply":"2024-01-26T16:21:51.871309Z"},"papermill":{"duration":0.049199,"end_time":"2024-01-26T16:21:51.875042","exception":false,"start_time":"2024-01-26T16:21:51.825843","status":"completed"},"tags":[]},"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>age</th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>gp</th>\n","      <th>pts</th>\n","      <th>reb</th>\n","      <th>ast</th>\n","      <th>net_rating</th>\n","      <th>oreb_pct</th>\n","      <th>dreb_pct</th>\n","      <th>usg_pct</th>\n","      <th>ts_pct</th>\n","      <th>ast_pct</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>1.000000</td>\n","      <td>-0.007904</td>\n","      <td>0.063561</td>\n","      <td>0.057442</td>\n","      <td>0.011353</td>\n","      <td>0.037386</td>\n","      <td>0.092359</td>\n","      <td>0.092896</td>\n","      <td>-0.055228</td>\n","      <td>0.018774</td>\n","      <td>-0.113882</td>\n","      <td>0.025245</td>\n","      <td>0.069029</td>\n","    </tr>\n","    <tr>\n","      <th>player_height</th>\n","      <td>-0.007904</td>\n","      <td>1.000000</td>\n","      <td>0.822141</td>\n","      <td>0.004963</td>\n","      <td>-0.055284</td>\n","      <td>0.424220</td>\n","      <td>-0.442781</td>\n","      <td>-0.003074</td>\n","      <td>0.589538</td>\n","      <td>0.614273</td>\n","      <td>-0.104011</td>\n","      <td>0.076797</td>\n","      <td>-0.608696</td>\n","    </tr>\n","    <tr>\n","      <th>player_weight</th>\n","      <td>0.063561</td>\n","      <td>0.822141</td>\n","      <td>1.000000</td>\n","      <td>0.022828</td>\n","      <td>-0.025023</td>\n","      <td>0.438112</td>\n","      <td>-0.371675</td>\n","      <td>0.003547</td>\n","      <td>0.599511</td>\n","      <td>0.605639</td>\n","      <td>-0.065811</td>\n","      <td>0.070992</td>\n","      <td>-0.521939</td>\n","    </tr>\n","    <tr>\n","      <th>gp</th>\n","      <td>0.057442</td>\n","      <td>0.004963</td>\n","      <td>0.022828</td>\n","      <td>1.000000</td>\n","      <td>0.536003</td>\n","      <td>0.471405</td>\n","      <td>0.382726</td>\n","      <td>0.248412</td>\n","      <td>-0.014763</td>\n","      <td>0.067911</td>\n","      <td>0.147248</td>\n","      <td>0.367876</td>\n","      <td>0.131720</td>\n","    </tr>\n","    <tr>\n","      <th>pts</th>\n","      <td>0.011353</td>\n","      <td>-0.055284</td>\n","      <td>-0.025023</td>\n","      <td>0.536003</td>\n","      <td>1.000000</td>\n","      <td>0.624509</td>\n","      <td>0.664320</td>\n","      <td>0.215199</td>\n","      <td>-0.125164</td>\n","      <td>0.055669</td>\n","      <td>0.641469</td>\n","      <td>0.373439</td>\n","      <td>0.337339</td>\n","    </tr>\n","    <tr>\n","      <th>reb</th>\n","      <td>0.037386</td>\n","      <td>0.424220</td>\n","      <td>0.438112</td>\n","      <td>0.471405</td>\n","      <td>0.624509</td>\n","      <td>1.000000</td>\n","      <td>0.247841</td>\n","      <td>0.187820</td>\n","      <td>0.407960</td>\n","      <td>0.611899</td>\n","      <td>0.232968</td>\n","      <td>0.313451</td>\n","      <td>-0.062654</td>\n","    </tr>\n","    <tr>\n","      <th>ast</th>\n","      <td>0.092359</td>\n","      <td>-0.442781</td>\n","      <td>-0.371675</td>\n","      <td>0.382726</td>\n","      <td>0.664320</td>\n","      <td>0.247841</td>\n","      <td>1.000000</td>\n","      <td>0.162865</td>\n","      <td>-0.337970</td>\n","      <td>-0.192146</td>\n","      <td>0.396505</td>\n","      <td>0.174494</td>\n","      <td>0.803150</td>\n","    </tr>\n","    <tr>\n","      <th>net_rating</th>\n","      <td>0.092896</td>\n","      <td>-0.003074</td>\n","      <td>0.003547</td>\n","      <td>0.248412</td>\n","      <td>0.215199</td>\n","      <td>0.187820</td>\n","      <td>0.162865</td>\n","      <td>1.000000</td>\n","      <td>0.055761</td>\n","      <td>0.047928</td>\n","      <td>0.026575</td>\n","      <td>0.313251</td>\n","      <td>0.063809</td>\n","    </tr>\n","    <tr>\n","      <th>oreb_pct</th>\n","      <td>-0.055228</td>\n","      <td>0.589538</td>\n","      <td>0.599511</td>\n","      <td>-0.014763</td>\n","      <td>-0.125164</td>\n","      <td>0.407960</td>\n","      <td>-0.337970</td>\n","      <td>0.055761</td>\n","      <td>1.000000</td>\n","      <td>0.555358</td>\n","      <td>-0.100091</td>\n","      <td>0.060572</td>\n","      <td>-0.424056</td>\n","    </tr>\n","    <tr>\n","      <th>dreb_pct</th>\n","      <td>0.018774</td>\n","      <td>0.614273</td>\n","      <td>0.605639</td>\n","      <td>0.067911</td>\n","      <td>0.055669</td>\n","      <td>0.611899</td>\n","      <td>-0.192146</td>\n","      <td>0.047928</td>\n","      <td>0.555358</td>\n","      <td>1.000000</td>\n","      <td>-0.007402</td>\n","      <td>0.105150</td>\n","      <td>-0.317477</td>\n","    </tr>\n","    <tr>\n","      <th>usg_pct</th>\n","      <td>-0.113882</td>\n","      <td>-0.104011</td>\n","      <td>-0.065811</td>\n","      <td>0.147248</td>\n","      <td>0.641469</td>\n","      <td>0.232968</td>\n","      <td>0.396505</td>\n","      <td>0.026575</td>\n","      <td>-0.100091</td>\n","      <td>-0.007402</td>\n","      <td>1.000000</td>\n","      <td>0.119323</td>\n","      <td>0.339286</td>\n","    </tr>\n","    <tr>\n","      <th>ts_pct</th>\n","      <td>0.025245</td>\n","      <td>0.076797</td>\n","      <td>0.070992</td>\n","      <td>0.367876</td>\n","      <td>0.373439</td>\n","      <td>0.313451</td>\n","      <td>0.174494</td>\n","      <td>0.313251</td>\n","      <td>0.060572</td>\n","      <td>0.105150</td>\n","      <td>0.119323</td>\n","      <td>1.000000</td>\n","      <td>0.022195</td>\n","    </tr>\n","    <tr>\n","      <th>ast_pct</th>\n","      <td>0.069029</td>\n","      <td>-0.608696</td>\n","      <td>-0.521939</td>\n","      <td>0.131720</td>\n","      <td>0.337339</td>\n","      <td>-0.062654</td>\n","      <td>0.803150</td>\n","      <td>0.063809</td>\n","      <td>-0.424056</td>\n","      <td>-0.317477</td>\n","      <td>0.339286</td>\n","      <td>0.022195</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                    age  player_height  player_weight        gp       pts  \\\n","age            1.000000      -0.007904       0.063561  0.057442  0.011353   \n","player_height -0.007904       1.000000       0.822141  0.004963 -0.055284   \n","player_weight  0.063561       0.822141       1.000000  0.022828 -0.025023   \n","gp             0.057442       0.004963       0.022828  1.000000  0.536003   \n","pts            0.011353      -0.055284      -0.025023  0.536003  1.000000   \n","reb            0.037386       0.424220       0.438112  0.471405  0.624509   \n","ast            0.092359      -0.442781      -0.371675  0.382726  0.664320   \n","net_rating     0.092896      -0.003074       0.003547  0.248412  0.215199   \n","oreb_pct      -0.055228       0.589538       0.599511 -0.014763 -0.125164   \n","dreb_pct       0.018774       0.614273       0.605639  0.067911  0.055669   \n","usg_pct       -0.113882      -0.104011      -0.065811  0.147248  0.641469   \n","ts_pct         0.025245       0.076797       0.070992  0.367876  0.373439   \n","ast_pct        0.069029      -0.608696      -0.521939  0.131720  0.337339   \n","\n","                    reb       ast  net_rating  oreb_pct  dreb_pct   usg_pct  \\\n","age            0.037386  0.092359    0.092896 -0.055228  0.018774 -0.113882   \n","player_height  0.424220 -0.442781   -0.003074  0.589538  0.614273 -0.104011   \n","player_weight  0.438112 -0.371675    0.003547  0.599511  0.605639 -0.065811   \n","gp             0.471405  0.382726    0.248412 -0.014763  0.067911  0.147248   \n","pts            0.624509  0.664320    0.215199 -0.125164  0.055669  0.641469   \n","reb            1.000000  0.247841    0.187820  0.407960  0.611899  0.232968   \n","ast            0.247841  1.000000    0.162865 -0.337970 -0.192146  0.396505   \n","net_rating     0.187820  0.162865    1.000000  0.055761  0.047928  0.026575   \n","oreb_pct       0.407960 -0.337970    0.055761  1.000000  0.555358 -0.100091   \n","dreb_pct       0.611899 -0.192146    0.047928  0.555358  1.000000 -0.007402   \n","usg_pct        0.232968  0.396505    0.026575 -0.100091 -0.007402  1.000000   \n","ts_pct         0.313451  0.174494    0.313251  0.060572  0.105150  0.119323   \n","ast_pct       -0.062654  0.803150    0.063809 -0.424056 -0.317477  0.339286   \n","\n","                 ts_pct   ast_pct  \n","age            0.025245  0.069029  \n","player_height  0.076797 -0.608696  \n","player_weight  0.070992 -0.521939  \n","gp             0.367876  0.131720  \n","pts            0.373439  0.337339  \n","reb            0.313451 -0.062654  \n","ast            0.174494  0.803150  \n","net_rating     0.313251  0.063809  \n","oreb_pct       0.060572 -0.424056  \n","dreb_pct       0.105150 -0.317477  \n","usg_pct        0.119323  0.339286  \n","ts_pct         1.000000  0.022195  \n","ast_pct        0.022195  1.000000  "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["heatmap_data.corr()"]},{"cell_type":"markdown","id":"c66314e8","metadata":{"papermill":{"duration":0.015061,"end_time":"2024-01-26T16:21:51.90584","exception":false,"start_time":"2024-01-26T16:21:51.890779","status":"completed"},"tags":[]},"source":["### Country-wise distribution of heights\n"," In this section we compare the height of players w.r.t to their countries to check which country got some good genetics"]},{"cell_type":"code","execution_count":14,"id":"f6b69bd9","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:51.938202Z","iopub.status.busy":"2024-01-26T16:21:51.937801Z","iopub.status.idle":"2024-01-26T16:21:51.95866Z","shell.execute_reply":"2024-01-26T16:21:51.957515Z"},"papermill":{"duration":0.039738,"end_time":"2024-01-26T16:21:51.960827","exception":false,"start_time":"2024-01-26T16:21:51.921089","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>player_height</th>\n","    </tr>\n","    <tr>\n","      <th>country</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Angola</th>\n","      <td>206.375000</td>\n","    </tr>\n","    <tr>\n","      <th>Argentina</th>\n","      <td>199.333134</td>\n","    </tr>\n","    <tr>\n","      <th>Australia</th>\n","      <td>199.796400</td>\n","    </tr>\n","    <tr>\n","      <th>Austria</th>\n","      <td>214.811429</td>\n","    </tr>\n","    <tr>\n","      <th>Bahamas</th>\n","      <td>202.292857</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>Ukraine</th>\n","      <td>210.255556</td>\n","    </tr>\n","    <tr>\n","      <th>United Kingdom</th>\n","      <td>203.305833</td>\n","    </tr>\n","    <tr>\n","      <th>Uruguay</th>\n","      <td>208.280000</td>\n","    </tr>\n","    <tr>\n","      <th>Venezuela</th>\n","      <td>198.120000</td>\n","    </tr>\n","    <tr>\n","      <th>Yugoslavia</th>\n","      <td>211.281818</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>82 rows × 1 columns</p>\n","</div>"],"text/plain":["                player_height\n","country                      \n","Angola             206.375000\n","Argentina          199.333134\n","Australia          199.796400\n","Austria            214.811429\n","Bahamas            202.292857\n","...                       ...\n","Ukraine            210.255556\n","United Kingdom     203.305833\n","Uruguay            208.280000\n","Venezuela          198.120000\n","Yugoslavia         211.281818\n","\n","[82 rows x 1 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["player_height_column = nba_data[['player_height','country']]\n","player_height_column.groupby('country').mean()\n"]},{"cell_type":"code","execution_count":15,"id":"6ffdb282","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:51.99346Z","iopub.status.busy":"2024-01-26T16:21:51.993057Z","iopub.status.idle":"2024-01-26T16:21:52.019479Z","shell.execute_reply":"2024-01-26T16:21:52.018363Z"},"papermill":{"duration":0.045598,"end_time":"2024-01-26T16:21:52.021886","exception":false,"start_time":"2024-01-26T16:21:51.976288","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["nba_data.duplicated(keep=False).sum()"]},{"cell_type":"markdown","id":"73db76e6","metadata":{"papermill":{"duration":0.016341,"end_time":"2024-01-26T16:21:52.054743","exception":false,"start_time":"2024-01-26T16:21:52.038402","status":"completed"},"tags":[]},"source":["## Linear Regression on Player Height and Player Weight\n","Simple linear regression, as there is high correlation between player_height and player_weight I've droped rest of the columns and split player_weight into X and player_height to y."]},{"cell_type":"code","execution_count":16,"id":"cf382e84","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.088822Z","iopub.status.busy":"2024-01-26T16:21:52.088085Z","iopub.status.idle":"2024-01-26T16:21:52.093975Z","shell.execute_reply":"2024-01-26T16:21:52.093079Z"},"papermill":{"duration":0.025481,"end_time":"2024-01-26T16:21:52.096116","exception":false,"start_time":"2024-01-26T16:21:52.070635","status":"completed"},"tags":[]},"outputs":[],"source":["data_phpw = nba_data.drop(['age','player_name','team_abbreviation','college','country','draft_year','draft_round','draft_number','season','gp','pts','reb','ast','net_rating','usg_pct','ts_pct','ast_pct','oreb_pct','dreb_pct'], axis=1)"]},{"cell_type":"code","execution_count":17,"id":"78e4217e","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.129669Z","iopub.status.busy":"2024-01-26T16:21:52.129278Z","iopub.status.idle":"2024-01-26T16:21:52.142468Z","shell.execute_reply":"2024-01-26T16:21:52.141317Z"},"papermill":{"duration":0.032779,"end_time":"2024-01-26T16:21:52.144768","exception":false,"start_time":"2024-01-26T16:21:52.111989","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>193.04</td>\n","      <td>94.800728</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>190.50</td>\n","      <td>86.182480</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>203.20</td>\n","      <td>103.418976</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>213.36</td>\n","      <td>119.748288</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>12839</th>\n","      <td>213.36</td>\n","      <td>127.005760</td>\n","    </tr>\n","    <tr>\n","      <th>12840</th>\n","      <td>213.36</td>\n","      <td>86.182480</td>\n","    </tr>\n","    <tr>\n","      <th>12841</th>\n","      <td>205.74</td>\n","      <td>102.511792</td>\n","    </tr>\n","    <tr>\n","      <th>12842</th>\n","      <td>208.28</td>\n","      <td>113.398000</td>\n","    </tr>\n","    <tr>\n","      <th>12843</th>\n","      <td>205.74</td>\n","      <td>102.965384</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12844 rows × 2 columns</p>\n","</div>"],"text/plain":["       player_height  player_weight\n","0             193.04      94.800728\n","1             190.50      86.182480\n","2             203.20     103.418976\n","3             203.20     102.058200\n","4             213.36     119.748288\n","...              ...            ...\n","12839         213.36     127.005760\n","12840         213.36      86.182480\n","12841         205.74     102.511792\n","12842         208.28     113.398000\n","12843         205.74     102.965384\n","\n","[12844 rows x 2 columns]"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["data_phpw"]},{"cell_type":"code","execution_count":18,"id":"1e9d3fcf","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.178575Z","iopub.status.busy":"2024-01-26T16:21:52.178153Z","iopub.status.idle":"2024-01-26T16:21:52.189481Z","shell.execute_reply":"2024-01-26T16:21:52.188136Z"},"papermill":{"duration":0.031061,"end_time":"2024-01-26T16:21:52.191833","exception":false,"start_time":"2024-01-26T16:21:52.160772","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>player_height</th>\n","      <td>1.000000</td>\n","      <td>0.822141</td>\n","    </tr>\n","    <tr>\n","      <th>player_weight</th>\n","      <td>0.822141</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               player_height  player_weight\n","player_height       1.000000       0.822141\n","player_weight       0.822141       1.000000"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["data_phpw.corr()"]},{"cell_type":"code","execution_count":19,"id":"e6b88208","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.225942Z","iopub.status.busy":"2024-01-26T16:21:52.225565Z","iopub.status.idle":"2024-01-26T16:21:52.25166Z","shell.execute_reply":"2024-01-26T16:21:52.250526Z"},"papermill":{"duration":0.046288,"end_time":"2024-01-26T16:21:52.254154","exception":false,"start_time":"2024-01-26T16:21:52.207866","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train: (9633, 1)\n","X_test: (3211, 1)\n","y_train: (9633,)\n","y_test: (3211,)\n","Coefficient: [0.60067646]\n","Intercept: 140.35271895460423\n"]}],"source":["X = data_phpw.iloc[:,1:].values\n","y = data_phpw.iloc[:,0].values\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=None,)\n","print(f'X_train: {X_train.shape}\\nX_test: {X_test.shape}\\ny_train: {y_train.shape}\\ny_test: {y_test.shape}')\n","regr = LinearRegression()\n","regr.fit(X_train, y_train)\n","print(f'Coefficient: {regr.coef_}')\n","print(f'Intercept: {regr.intercept_}')"]},{"cell_type":"code","execution_count":20,"id":"1ea7b24a","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.290462Z","iopub.status.busy":"2024-01-26T16:21:52.289904Z","iopub.status.idle":"2024-01-26T16:21:52.300394Z","shell.execute_reply":"2024-01-26T16:21:52.298992Z"},"papermill":{"duration":0.031532,"end_time":"2024-01-26T16:21:52.302789","exception":false,"start_time":"2024-01-26T16:21:52.271257","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Absolute Error(MAE): 4.137050763002179\n","Residual Sum of Squares(MSE): 27.265069542198695\n","R2-Score: 0.6770646603509813\n"]}],"source":["y_pred = np.round(regr.predict(X_test), decimals=2)\n","print(f'Mean Absolute Error(MAE): {metrics.mean_absolute_error(y_test, y_pred)}')\n","print(f'Residual Sum of Squares(MSE): {metrics.mean_squared_error(y_test, y_pred)}')\n","print(f'R2-Score: {metrics.r2_score(y_test, y_pred)}')"]},{"cell_type":"code","execution_count":21,"id":"80802bd3","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.338674Z","iopub.status.busy":"2024-01-26T16:21:52.337761Z","iopub.status.idle":"2024-01-26T16:21:52.656994Z","shell.execute_reply":"2024-01-26T16:21:52.655799Z"},"papermill":{"duration":0.340164,"end_time":"2024-01-26T16:21:52.659789","exception":false,"start_time":"2024-01-26T16:21:52.319625","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7812e1153a60>]"]},"execution_count":21,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ratio1 = y_test / y_pred\n","plt.plot(ratio1)"]},{"cell_type":"markdown","id":"0f5849a2","metadata":{"papermill":{"duration":0.016582,"end_time":"2024-01-26T16:21:52.693348","exception":false,"start_time":"2024-01-26T16:21:52.676766","status":"completed"},"tags":[]},"source":["## Linear regression on Player Age and their respective picking (draft) years\n","\n","We have only used the age and draft_year column here, so we drop rest of the columns"]},{"cell_type":"code","execution_count":22,"id":"6b17131e","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.728975Z","iopub.status.busy":"2024-01-26T16:21:52.728343Z","iopub.status.idle":"2024-01-26T16:21:52.734929Z","shell.execute_reply":"2024-01-26T16:21:52.734026Z"},"papermill":{"duration":0.027226,"end_time":"2024-01-26T16:21:52.737267","exception":false,"start_time":"2024-01-26T16:21:52.710041","status":"completed"},"tags":[]},"outputs":[],"source":["data_pady = nba_data.drop(['player_height','player_name','team_abbreviation','college','country','player_weight','draft_round','draft_number','season','gp','pts','reb','ast','net_rating','usg_pct','ts_pct','ast_pct','oreb_pct','dreb_pct'], axis=1)"]},{"cell_type":"code","execution_count":23,"id":"e3d8d514","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.773675Z","iopub.status.busy":"2024-01-26T16:21:52.773022Z","iopub.status.idle":"2024-01-26T16:21:52.785902Z","shell.execute_reply":"2024-01-26T16:21:52.784721Z"},"papermill":{"duration":0.033878,"end_time":"2024-01-26T16:21:52.788179","exception":false,"start_time":"2024-01-26T16:21:52.754301","status":"completed"},"tags":[]},"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>age</th>\n","      <th>draft_year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>22.0</td>\n","      <td>1996</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>28.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>26.0</td>\n","      <td>1993</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>30.0</td>\n","      <td>1989</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>12839</th>\n","      <td>29.0</td>\n","      <td>2014</td>\n","    </tr>\n","    <tr>\n","      <th>12840</th>\n","      <td>20.0</td>\n","      <td>Undrafted</td>\n","    </tr>\n","    <tr>\n","      <th>12841</th>\n","      <td>25.0</td>\n","      <td>2017</td>\n","    </tr>\n","    <tr>\n","      <th>12842</th>\n","      <td>24.0</td>\n","      <td>2021</td>\n","    </tr>\n","    <tr>\n","      <th>12843</th>\n","      <td>33.0</td>\n","      <td>Undrafted</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12844 rows × 2 columns</p>\n","</div>"],"text/plain":["        age draft_year\n","0      22.0       1996\n","1      28.0       1994\n","2      26.0       1993\n","3      30.0       1989\n","4      23.0       1995\n","...     ...        ...\n","12839  29.0       2014\n","12840  20.0  Undrafted\n","12841  25.0       2017\n","12842  24.0       2021\n","12843  33.0  Undrafted\n","\n","[12844 rows x 2 columns]"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["data_pady"]},{"cell_type":"markdown","id":"8d25bf19","metadata":{"papermill":{"duration":0.017042,"end_time":"2024-01-26T16:21:52.822055","exception":false,"start_time":"2024-01-26T16:21:52.805013","status":"completed"},"tags":[]},"source":["Since, we don't need to consider players who were not drafted, therefore, I am just eliminating those values too."]},{"cell_type":"code","execution_count":24,"id":"0a287734","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.858459Z","iopub.status.busy":"2024-01-26T16:21:52.858058Z","iopub.status.idle":"2024-01-26T16:21:52.866494Z","shell.execute_reply":"2024-01-26T16:21:52.86548Z"},"papermill":{"duration":0.02975,"end_time":"2024-01-26T16:21:52.86889","exception":false,"start_time":"2024-01-26T16:21:52.83914","status":"completed"},"tags":[]},"outputs":[],"source":["to_drop = data_pady[((data_pady.draft_year == 'Undrafted'))].index\n","new_data_pady= data_pady.drop(to_drop)"]},{"cell_type":"code","execution_count":25,"id":"1a72c73f","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.905226Z","iopub.status.busy":"2024-01-26T16:21:52.904587Z","iopub.status.idle":"2024-01-26T16:21:52.91954Z","shell.execute_reply":"2024-01-26T16:21:52.918306Z"},"papermill":{"duration":0.035931,"end_time":"2024-01-26T16:21:52.921981","exception":false,"start_time":"2024-01-26T16:21:52.88605","status":"completed"},"tags":[]},"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>age</th>\n","      <th>draft_year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>22.0</td>\n","      <td>1996</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>28.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>26.0</td>\n","      <td>1993</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>30.0</td>\n","      <td>1989</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>33.0</td>\n","      <td>1985</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>26.0</td>\n","      <td>1993</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>30.0</td>\n","      <td>1989</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>24.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>24.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>22.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>31.0</td>\n","      <td>1988</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>29.0</td>\n","      <td>1991</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>33.0</td>\n","      <td>1986</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>28.0</td>\n","      <td>1990</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>26.0</td>\n","      <td>1993</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>25.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>24.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>34.0</td>\n","      <td>1984</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>31.0</td>\n","      <td>1988</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>31.0</td>\n","      <td>1989</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>33.0</td>\n","      <td>1987</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>28.0</td>\n","      <td>1990</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>32.0</td>\n","      <td>1988</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>36.0</td>\n","      <td>1982</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>29.0</td>\n","      <td>1992</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>28.0</td>\n","      <td>1991</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>27.0</td>\n","      <td>1992</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>27.0</td>\n","      <td>1992</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>34.0</td>\n","      <td>1983</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>31.0</td>\n","      <td>1988</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>27.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>25.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>29.0</td>\n","      <td>1991</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>26.0</td>\n","      <td>1994</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>24.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>24.0</td>\n","      <td>1995</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>21.0</td>\n","      <td>1996</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>29.0</td>\n","      <td>1993</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>29.0</td>\n","      <td>1990</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     age draft_year\n","0   22.0       1996\n","1   28.0       1994\n","2   26.0       1993\n","3   30.0       1989\n","4   23.0       1995\n","5   33.0       1985\n","6   26.0       1993\n","7   30.0       1989\n","8   24.0       1994\n","9   24.0       1994\n","10  22.0       1995\n","11  31.0       1988\n","12  29.0       1991\n","13  33.0       1986\n","14  28.0       1990\n","15  26.0       1993\n","16  25.0       1994\n","17  24.0       1995\n","18  34.0       1984\n","19  31.0       1988\n","20  31.0       1989\n","21  33.0       1987\n","22  28.0       1990\n","23  32.0       1988\n","24  36.0       1982\n","25  29.0       1992\n","26  28.0       1991\n","27  27.0       1992\n","28  27.0       1992\n","30  34.0       1983\n","31  31.0       1988\n","32  27.0       1994\n","33  25.0       1994\n","34  29.0       1991\n","35  26.0       1994\n","36  24.0       1995\n","37  24.0       1995\n","38  21.0       1996\n","40  29.0       1993\n","42  29.0       1990"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["new_data_pady.head(40)"]},{"cell_type":"markdown","id":"8dc873e2","metadata":{"papermill":{"duration":0.016941,"end_time":"2024-01-26T16:21:52.956266","exception":false,"start_time":"2024-01-26T16:21:52.939325","status":"completed"},"tags":[]},"source":["Here, is a key relation between respective ages and draft_years"]},{"cell_type":"code","execution_count":26,"id":"26afd45e","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:52.99328Z","iopub.status.busy":"2024-01-26T16:21:52.992841Z","iopub.status.idle":"2024-01-26T16:21:53.550763Z","shell.execute_reply":"2024-01-26T16:21:53.549636Z"},"papermill":{"duration":0.580796,"end_time":"2024-01-26T16:21:53.554741","exception":false,"start_time":"2024-01-26T16:21:52.973945","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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size 1500x1500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["dummy_relation = new_data_pady.head(3500)\n","plt.figure(figsize=(15,15))\n","ax = plt.axes()\n","\n","ax.scatter(dummy_relation.draft_year, dummy_relation.age,s=10,c='r')\n","\n","ax.set(xlabel='Draft Year',\n","       ylabel='Age',\n","       title='Draft Year vs Age');"]},{"cell_type":"code","execution_count":27,"id":"2aa19dcb","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:53.600233Z","iopub.status.busy":"2024-01-26T16:21:53.59985Z","iopub.status.idle":"2024-01-26T16:21:53.615281Z","shell.execute_reply":"2024-01-26T16:21:53.614116Z"},"papermill":{"duration":0.038572,"end_time":"2024-01-26T16:21:53.617591","exception":false,"start_time":"2024-01-26T16:21:53.579019","status":"completed"},"tags":[]},"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>age</th>\n","      <th>draft_year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>1.000000</td>\n","      <td>-0.559036</td>\n","    </tr>\n","    <tr>\n","      <th>draft_year</th>\n","      <td>-0.559036</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 age  draft_year\n","age         1.000000   -0.559036\n","draft_year -0.559036    1.000000"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["new_data_pady.corr()"]},{"cell_type":"markdown","id":"00545c24","metadata":{"papermill":{"duration":0.019196,"end_time":"2024-01-26T16:21:53.656491","exception":false,"start_time":"2024-01-26T16:21:53.637295","status":"completed"},"tags":[]},"source":["Here, we see they have a negative relationship, so the higher the year, the lesser the age, which is absolutely right, a 18 year old boy born in 1980 would be drafted in 1998 and a 19 year old boy now, would have been drafted a year earlier."]},{"cell_type":"code","execution_count":28,"id":"df5359b4","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:53.697393Z","iopub.status.busy":"2024-01-26T16:21:53.69661Z","iopub.status.idle":"2024-01-26T16:21:53.710453Z","shell.execute_reply":"2024-01-26T16:21:53.709274Z"},"papermill":{"duration":0.037859,"end_time":"2024-01-26T16:21:53.712996","exception":false,"start_time":"2024-01-26T16:21:53.675137","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["A_train: (7864, 1)\n","A_test: (2622, 1)\n","b_train: (7864,)\n","b_test: (2622,)\n","Coefficient: [-0.27625221]\n","Intercept: 580.6608616379963\n"]}],"source":["A = new_data_pady.iloc[:,1:].values\n","b = new_data_pady.iloc[:,0].values\n","A_train, A_test, b_train, b_test = train_test_split(A, b, test_size=0.25, random_state=None,)\n","print(f'A_train: {A_train.shape}\\nA_test: {A_test.shape}\\nb_train: {b_train.shape}\\nb_test: {b_test.shape}')\n","regr = LinearRegression()\n","regr.fit(A_train, b_train)\n","print(f'Coefficient: {regr.coef_}')\n","print(f'Intercept: {regr.intercept_}')"]},{"cell_type":"code","execution_count":29,"id":"4d219d88","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:53.754408Z","iopub.status.busy":"2024-01-26T16:21:53.753963Z","iopub.status.idle":"2024-01-26T16:21:53.76396Z","shell.execute_reply":"2024-01-26T16:21:53.762729Z"},"papermill":{"duration":0.033429,"end_time":"2024-01-26T16:21:53.766282","exception":false,"start_time":"2024-01-26T16:21:53.732853","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Absolute Error(MAE): 3.001586575133486\n","Residual Sum of Squares(MSE): 13.709687337909992\n","R2-Score: 0.31807197503699114\n"]}],"source":["b_pred = np.round(regr.predict(A_test), decimals=2)\n","print(f'Mean Absolute Error(MAE): {metrics.mean_absolute_error(b_test, b_pred)}')\n","print(f'Residual Sum of Squares(MSE): {metrics.mean_squared_error(b_test, b_pred)}')\n","print(f'R2-Score: {metrics.r2_score(b_test, b_pred)}')"]},{"cell_type":"code","execution_count":30,"id":"a4e69a00","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:53.805096Z","iopub.status.busy":"2024-01-26T16:21:53.804688Z","iopub.status.idle":"2024-01-26T16:21:54.100571Z","shell.execute_reply":"2024-01-26T16:21:54.099229Z"},"papermill":{"duration":0.318577,"end_time":"2024-01-26T16:21:54.103477","exception":false,"start_time":"2024-01-26T16:21:53.7849","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7812e109f5e0>]"]},"execution_count":30,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ratio2 = b_test / b_pred\n","plt.plot(ratio2)"]},{"cell_type":"code","execution_count":31,"id":"4a27f747","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:54.144609Z","iopub.status.busy":"2024-01-26T16:21:54.144205Z","iopub.status.idle":"2024-01-26T16:21:54.151159Z","shell.execute_reply":"2024-01-26T16:21:54.149918Z"},"papermill":{"duration":0.030873,"end_time":"2024-01-26T16:21:54.153542","exception":false,"start_time":"2024-01-26T16:21:54.122669","status":"completed"},"tags":[]},"outputs":[],"source":["datx =nba_data.drop(['oreb_pct','age','player_name','team_abbreviation','college','country','draft_year','draft_round','draft_number','season','gp','pts','reb','ast','net_rating','usg_pct','ts_pct','ast_pct'], axis=1)"]},{"cell_type":"code","execution_count":32,"id":"d1033840","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:54.193552Z","iopub.status.busy":"2024-01-26T16:21:54.193125Z","iopub.status.idle":"2024-01-26T16:21:54.206664Z","shell.execute_reply":"2024-01-26T16:21:54.205481Z"},"papermill":{"duration":0.036464,"end_time":"2024-01-26T16:21:54.20897","exception":false,"start_time":"2024-01-26T16:21:54.172506","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>player_height</th>\n","      <th>player_weight</th>\n","      <th>dreb_pct</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>193.04</td>\n","      <td>94.800728</td>\n","      <td>0.071</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>190.50</td>\n","      <td>86.182480</td>\n","      <td>0.111</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>203.20</td>\n","      <td>103.418976</td>\n","      <td>0.185</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>203.20</td>\n","      <td>102.058200</td>\n","      <td>0.111</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>213.36</td>\n","      <td>119.748288</td>\n","      <td>0.169</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>12839</th>\n","      <td>213.36</td>\n","      <td>127.005760</td>\n","      <td>0.243</td>\n","    </tr>\n","    <tr>\n","      <th>12840</th>\n","      <td>213.36</td>\n","      <td>86.182480</td>\n","      <td>0.065</td>\n","    </tr>\n","    <tr>\n","      <th>12841</th>\n","      <td>205.74</td>\n","      <td>102.511792</td>\n","      <td>0.180</td>\n","    </tr>\n","    <tr>\n","      <th>12842</th>\n","      <td>208.28</td>\n","      <td>113.398000</td>\n","      <td>0.175</td>\n","    </tr>\n","    <tr>\n","      <th>12843</th>\n","      <td>205.74</td>\n","      <td>102.965384</td>\n","      <td>0.164</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12844 rows × 3 columns</p>\n","</div>"],"text/plain":["       player_height  player_weight  dreb_pct\n","0             193.04      94.800728     0.071\n","1             190.50      86.182480     0.111\n","2             203.20     103.418976     0.185\n","3             203.20     102.058200     0.111\n","4             213.36     119.748288     0.169\n","...              ...            ...       ...\n","12839         213.36     127.005760     0.243\n","12840         213.36      86.182480     0.065\n","12841         205.74     102.511792     0.180\n","12842         208.28     113.398000     0.175\n","12843         205.74     102.965384     0.164\n","\n","[12844 rows x 3 columns]"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["datx"]},{"cell_type":"markdown","id":"a29b7e5a","metadata":{"papermill":{"duration":0.018913,"end_time":"2024-01-26T16:21:54.247111","exception":false,"start_time":"2024-01-26T16:21:54.228198","status":"completed"},"tags":[]},"source":["### Comparing Height, Weight and Defensive Rebounding Percentage \n"," Let's check if it is correct to say that players with higher height have good defense advantage. \n"," \n","**dreb_pct** -> Defensive Rebounding Percentage Definition. The percentage of available defensive rebounds a player or team obtains while on the floor."]},{"cell_type":"code","execution_count":33,"id":"8997a232","metadata":{"execution":{"iopub.execute_input":"2024-01-26T16:21:54.291428Z","iopub.status.busy":"2024-01-26T16:21:54.290031Z","iopub.status.idle":"2024-01-26T16:22:12.516145Z","shell.execute_reply":"2024-01-26T16:22:12.515054Z"},"papermill":{"duration":18.252932,"end_time":"2024-01-26T16:22:12.519265","exception":false,"start_time":"2024-01-26T16:21:54.266333","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig = plt.figure()\n","ax = fig.add_subplot(111, projection='3d')\n","\n","x = datx['player_height']\n","y = datx['player_weight']\n","z = datx['dreb_pct']\n","\n","ax.scatter(x, y, z)\n","\n","A = np.vstack([x, y, np.ones_like(x)]).T\n","plane_coef, _, _, _ = np.linalg.lstsq(A, z, rcond=None)\n","\n","x_plane, y_plane = np.meshgrid(x, y)\n","z_plane = plane_coef[0] * x_plane + plane_coef[1] * y_plane + plane_coef[2]\n","\n","ax.plot_surface(x_plane, y_plane, z_plane, alpha=0.5)\n","\n","ax.set_xlabel('Player Height')\n","ax.set_ylabel('Player Weight')\n","ax.set_zlabel('dreb_pct')\n","plt.title('Multiple Linear Regression on Height, Weight and Defensive Rebounding Percentage ')\n","\n","plt.show()"]},{"cell_type":"markdown","id":"fa2dfd09","metadata":{"papermill":{"duration":0.023045,"end_time":"2024-01-26T16:22:12.56463","exception":false,"start_time":"2024-01-26T16:22:12.541585","status":"completed"},"tags":[]},"source":["# Conclusion\n","  I am still looking to explore more about this data. Kindly suggest some cool ideas I should try, and **please upvote !**"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":98271,"sourceId":6687390,"sourceType":"datasetVersion"}],"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"},"papermill":{"default_parameters":{},"duration":29.623305,"end_time":"2024-01-26T16:22:13.308385","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-01-26T16:21:43.68508","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}