Jupyter_Notebook_Projects/Predict_NBA_player_Points.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/M-MSilva/Predict-NBA-player-Points_End-to-end-Project/blob/master/Jupyter_Notebook_Projects/Predict_NBA_player_Points.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Predict NBA player Points - end-to-end project"
      ],
      "metadata": {
        "id": "vMJIcACohNPx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In addition to creating a project to predict the points of a basketball player, we will answer business questions:\n",
        "\n",
        "1. Which age group, team and position score the most points per game? \n",
        "2. What exactly are the athletes who score the most and give the most assists simultaneously?\n",
        "3. What is the age group, time and position that makes the most assists? \n",
        "4. What exactly are the athletes who have the most turnovers and scores simultaneously?\n",
        "5. What age group, team and position do the most turnovers? \n",
        "\n",
        "Such questions will be answered in section 5 on data visualization.\n",
        "\n"
      ],
      "metadata": {
        "id": "Pf8woH3eAK9t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1 - Initial Requirements"
      ],
      "metadata": {
        "id": "XReGWJRWhmcW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Always at the beginning of a project we must import some elementary libraries."
      ],
      "metadata": {
        "id": "ne81PXxAa9g_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Python ≥3.5 is required\n",
        "import sys\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "#images\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set_theme()"
      ],
      "metadata": {
        "id": "7oj0JAXmxQ7J"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 - Download Data"
      ],
      "metadata": {
        "id": "YGpvpfKZ_Ec3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this step, we get the data."
      ],
      "metadata": {
        "id": "x6Xzwfptit47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Dataset = pd.read_csv('https://raw.githubusercontent.com/M-MSilva/Predict-NBA-player-Points-End-to-end-Project/master/Dataset/NBA_Athletes.csv'\n",
        "                          ,decimal='.', thousands=',',encoding='utf-8',index_col=0)"
      ],
      "metadata": {
        "id": "Wh5goW2s_XPi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "rearrange some information"
      ],
      "metadata": {
        "id": "LVdLMRV81uPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#same categories\n",
        "Dataset['POS'].replace({'C-F':'F-C','G-F':'F-G'},inplace=True)\n",
        "\n",
        "#new dataset that will be used for prediction, where we removed the assist attribute per game, to avoid data leakage\n",
        "NbaDataset = Dataset.drop(\"APG\", axis=1)"
      ],
      "metadata": {
        "id": "eonpafEen5bU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 - Initial Data Insights"
      ],
      "metadata": {
        "id": "NquQwt4BpGzy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After all the initial setup we are ready to go to work and extract some precious information from our data. First, we use the head() method on the dataframe to better understand the dataset. As already discussed, this dataset was extracted using **web scraping** from the site 'https://www.nbastuffer.com/player-stats/', where we extracted data from 2021 to 2017."
      ],
      "metadata": {
        "id": "QfW_k0i6qBN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Dataset.head()"
      ],
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      "execution_count": 4,
      "outputs": [
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              "           FULL NAME TEAM  POS    AGE  GP   MPG  MIN%  USG%   TO%  FTA    FT%  \\\n",
              "0   Precious Achiuwa  Tor    F  22.51  63  23.6  49.3  18.2  11.5  109  0.587   \n",
              "1       Steven Adams  Mem    C  28.67  68  26.5  55.1  12.3  19.7  184  0.543   \n",
              "2        Bam Adebayo  Mia  F-C  24.68  47  32.9  68.5  25.2  14.6  276  0.757   \n",
              "3       Santi Aldama  Mem  F-C  21.20  27  10.2  21.4  17.0   7.8   20  0.600   \n",
              "4  LaMarcus Aldridge  Bro  F-C  36.68  45  22.8  47.6  22.7   7.8  102  0.873   \n",
              "\n",
              "   2PA  3PA   PPG   RPG  TRB%  APG   SPG   BPG  TOPG  \n",
              "0  398  114   8.8   6.9  15.7  1.1  0.51  0.54  1.16  \n",
              "1  349    1   7.1   9.9  19.4  3.2  0.82  0.75  1.56  \n",
              "2  622    5  19.0  10.3  17.6  3.5  1.49  0.83  2.72  \n",
              "3   58   39   3.3   2.4  12.0  0.5  0.07  0.26  0.33  \n",
              "4  410   45  13.5   5.6  13.4  0.9  0.31  1.02  0.93  "
            ],
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              "      <th>FULL NAME</th>\n",
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              "      <td>Precious Achiuwa</td>\n",
              "      <td>Tor</td>\n",
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              "      <td>22.51</td>\n",
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              "      <td>23.6</td>\n",
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              "      <td>18.2</td>\n",
              "      <td>11.5</td>\n",
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              "      <td>0.51</td>\n",
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              "      <td>1.16</td>\n",
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              "      <th>1</th>\n",
              "      <td>Steven Adams</td>\n",
              "      <td>Mem</td>\n",
              "      <td>C</td>\n",
              "      <td>28.67</td>\n",
              "      <td>68</td>\n",
              "      <td>26.5</td>\n",
              "      <td>55.1</td>\n",
              "      <td>12.3</td>\n",
              "      <td>19.7</td>\n",
              "      <td>184</td>\n",
              "      <td>0.543</td>\n",
              "      <td>349</td>\n",
              "      <td>1</td>\n",
              "      <td>7.1</td>\n",
              "      <td>9.9</td>\n",
              "      <td>19.4</td>\n",
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              "      <td>0.82</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.56</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bam Adebayo</td>\n",
              "      <td>Mia</td>\n",
              "      <td>F-C</td>\n",
              "      <td>24.68</td>\n",
              "      <td>47</td>\n",
              "      <td>32.9</td>\n",
              "      <td>68.5</td>\n",
              "      <td>25.2</td>\n",
              "      <td>14.6</td>\n",
              "      <td>276</td>\n",
              "      <td>0.757</td>\n",
              "      <td>622</td>\n",
              "      <td>5</td>\n",
              "      <td>19.0</td>\n",
              "      <td>10.3</td>\n",
              "      <td>17.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.49</td>\n",
              "      <td>0.83</td>\n",
              "      <td>2.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Santi Aldama</td>\n",
              "      <td>Mem</td>\n",
              "      <td>F-C</td>\n",
              "      <td>21.20</td>\n",
              "      <td>27</td>\n",
              "      <td>10.2</td>\n",
              "      <td>21.4</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7.8</td>\n",
              "      <td>20</td>\n",
              "      <td>0.600</td>\n",
              "      <td>58</td>\n",
              "      <td>39</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.33</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LaMarcus Aldridge</td>\n",
              "      <td>Bro</td>\n",
              "      <td>F-C</td>\n",
              "      <td>36.68</td>\n",
              "      <td>45</td>\n",
              "      <td>22.8</td>\n",
              "      <td>47.6</td>\n",
              "      <td>22.7</td>\n",
              "      <td>7.8</td>\n",
              "      <td>102</td>\n",
              "      <td>0.873</td>\n",
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              "      <td>45</td>\n",
              "      <td>13.5</td>\n",
              "      <td>5.6</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.02</td>\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "It is worth mentioning that the features were removed from the Dataframe to avoid too many data leaks such as the EFG% which is the Field Goal Effective Percentage."
      ],
      "metadata": {
        "id": "nDX_dtz5qint"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 - Glossary"
      ],
      "metadata": {
        "id": "4ZH-8Tqzzgiy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the DataFrame above, many features are self-explanatory, so here's some clarification:\n",
        "\n",
        "\n",
        "\n",
        "*   GP - The number of matches an athlete has played; \n",
        "*   MPG -  Minutes per game of the team used by a player; \n",
        "*   MIN% - Percentage of team minutes per game used by a player while he was on the court; \n",
        "*   USG% - Percentage of team plays used by a player while he was on the court. \n",
        "*   TO% - Number of turnover a player makes per 100 possessions; \n",
        "*   FTA - The number of free throws a player attempts; \n",
        "*   FT% - The percentage of free-throw attempts a player makes;\n",
        "*   2PA -The number of 2-point field goals a player attempts; \n",
        "*   3PA - The number of 3-point field goals a player attempts;\n",
        "*   PPG - Points per Game (TARGET VALUE); \n",
        "*   RPG - Rebounds per game; \n",
        "*   TRB% - Percentage of available rebounds grabbed by player while player is on court; \n",
        "*   SPG - Steals Per Game; \n",
        "*   BPG - Blocks per Game; \n",
        "*   TOPG -  Turnovers per Game; \n",
        "*   APG - Assistance per Game.\n"
      ],
      "metadata": {
        "id": "rFdEypp8wsjE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 - Dataset Summary"
      ],
      "metadata": {
        "id": "ZsrUxsyb9dXJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After understanding some attributes, we can get a description of the data with the info() method."
      ],
      "metadata": {
        "id": "E6e4hDHQ9pag"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Dataset.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "n63qrTT39VtE",
        "outputId": "3ca020ec-3813-40e1-9eef-ae2df42efe88"
      },
      "execution_count": 5,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1573 entries, 0 to 1572\n",
            "Data columns (total 20 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   FULL NAME  1573 non-null   object \n",
            " 1   TEAM       1573 non-null   object \n",
            " 2   POS        1573 non-null   object \n",
            " 3   AGE        1573 non-null   float64\n",
            " 4   GP         1573 non-null   int64  \n",
            " 5   MPG        1573 non-null   float64\n",
            " 6   MIN%       1573 non-null   float64\n",
            " 7   USG%       1573 non-null   float64\n",
            " 8   TO%        1529 non-null   float64\n",
            " 9   FTA        1573 non-null   int64  \n",
            " 10  FT%        1573 non-null   float64\n",
            " 11  2PA        1573 non-null   int64  \n",
            " 12  3PA        1573 non-null   int64  \n",
            " 13  PPG        1573 non-null   float64\n",
            " 14  RPG        1573 non-null   float64\n",
            " 15  TRB%       1573 non-null   float64\n",
            " 16  APG        1573 non-null   float64\n",
            " 17  SPG        1573 non-null   float64\n",
            " 18  BPG        1573 non-null   float64\n",
            " 19  TOPG       1573 non-null   float64\n",
            "dtypes: float64(13), int64(4), object(3)\n",
            "memory usage: 258.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this, we understand that our dataset has 3 categorical and 17 numeric attributes, totaling 20 columns with 1573 rows. I would really like to collect more data through web scraping, however, the structure of the site 'https://www.nbastuffer.com/player-stats/', changes a bit as the date goes beyond 2017."
      ],
      "metadata": {
        "id": "EjV4NhE-9_Fh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 - Understanding the data"
      ],
      "metadata": {
        "id": "5tTAWCPyAQar"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we know what our numerical attributes are, we can plot them in the form of a histogram. Let's plot various player information on the x-axis by the frequency density on the y-axis."
      ],
      "metadata": {
        "id": "2UWzFRypcpWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#a histogram makes more sense with numerical data.\n",
        "dataset_num = NbaDataset.select_dtypes(include=[np.number])"
      ],
      "metadata": {
        "id": "PmcQWkPijN5R"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#color map\n",
        "cmap = mpl.cm.Dark2\n",
        "\n",
        "fig=plt.figure(figsize=(18,14))\n",
        "\n",
        "#list with the x label\n",
        "measurement = ['years old','number of games played','minutes per game (min)','percentage of minutes per game(min)',\n",
        "               'usage rate','turnover/100 pos.','free throws','percentage of free thow']\n",
        "\n",
        "#list of data we will plot\n",
        "data =['AGE','GP','MPG','MIN%','USG%','TO%','FTA','FT%']\n",
        "\n",
        "\n",
        "#loop that plots all the charts we want, using the hist() method\n",
        "for i, x, in zip(enumerate(dataset_num),measurement):\n",
        "  ax=fig.add_subplot(4,4,i[0]+1)\n",
        "  NbaDataset[i[1]].hist(bins=50,ax=ax,color=cmap(i[0]))\n",
        "  ax.set_xlabel(x)\n",
        "  ax.set_ylabel('Frequency Density')\n",
        "  ax.set_title(i[1])\n",
        "fig.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "bPTRIiTDi6n5",
        "outputId": "8d1cc619-0370-42d4-a685-3fce4e5416dc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1008 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In these graphs we understand that the high frequencies of number of games played, minutes per game, usage rate, turnover, free throws happen when values on the x axis revolve around respectively 4.28, 3.33 min, 16%, 2, 13."
      ],
      "metadata": {
        "id": "o_Ahr0lmts58"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In addition to these data, we also plot the remaining data."
      ],
      "metadata": {
        "id": "8abH9bQs94xK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = ['2PA','3PA','PPG','RPG','TRB%','SPG','BPG','TOPG']\n",
        "\n",
        "measurement = ['2-point field goals attempts','3-point field goals attempts','points per game','rebounds per game','% of rebounds grabbed by player'\n",
        "        ,'steals per game','Blocks per Game','turnover per Game']\n",
        "\n",
        "\n",
        "cmap = mpl.cm.Dark2\n",
        "\n",
        "fig=plt.figure(figsize=(18,14))\n",
        "\n",
        "Newdataset_num = dataset_num.iloc[:,8:16]\n",
        "\n",
        "for i, x, in zip(enumerate(Newdataset_num ),measurement):\n",
        "  ax=fig.add_subplot(4,4,i[0]+1)\n",
        "  NbaDataset[i[1]].hist(bins=50,ax=ax,color=cmap(i[0]))\n",
        "  ax.set_xlabel(x)\n",
        "  ax.set_ylabel('Frequency Density')\n",
        "  ax.set_title(i[1])\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "-nYzTtIri6Ta",
        "outputId": "39401c9a-9390-4c44-fb3a-cbe276d7fe0c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1008 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here most values on the x-axis are low when they have high frequencies, even steals per game is around 2.4. It is worth noting that in these two graphs we have a histogram with a long tail, because of this, we will treat our data set with caution later."
      ],
      "metadata": {
        "id": "9bF0Bl-e-S4h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To understand the data in an easier way, we will use the describe() method:"
      ],
      "metadata": {
        "id": "d1y0LDY9_LvS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "BTARSzCB8LIl",
        "outputId": "4d881a4b-06de-4888-d51e-fe2d4652fef3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               AGE           GP          MPG         MIN%         USG%  \\\n",
              "count  1573.000000  1573.000000  1573.000000  1573.000000  1573.000000   \n",
              "mean     26.993484    19.089002    18.794469    39.149841    18.128163   \n",
              "std       4.223815    20.440654    11.401266    23.756698     7.529271   \n",
              "min      19.000000     1.000000     0.200000     0.400000     0.000000   \n",
              "25%      23.670000     5.000000     8.600000    17.900000    13.500000   \n",
              "50%      26.530000    10.000000    17.900000    37.300000    17.400000   \n",
              "75%      29.870000    26.000000    29.000000    60.400000    22.100000   \n",
              "max      41.780000    73.000000    41.900000    87.300000    63.700000   \n",
              "\n",
              "               TO%          FTA          FT%          2PA          3PA  \\\n",
              "count  1529.000000  1573.000000  1573.000000  1573.000000  1573.000000   \n",
              "mean     10.087659    39.746980     0.620615    95.001271    62.460267   \n",
              "std       8.856864    69.469204     0.325143   152.547152   103.187201   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       4.300000     2.000000     0.500000     7.000000     2.000000   \n",
              "50%      10.000000    13.000000     0.744000    32.000000    20.000000   \n",
              "75%      14.100000    44.000000     0.842000   110.000000    69.000000   \n",
              "max     100.000000   691.000000     1.000000  1238.000000   750.000000   \n",
              "\n",
              "               PPG          RPG         TRB%          SPG          BPG  \\\n",
              "count  1573.000000  1573.000000  1573.000000  1573.000000  1573.000000   \n",
              "mean      8.172600     3.413795    10.107883     0.565105     0.358010   \n",
              "std       7.136968     2.693283     6.387825     0.488581     0.427527   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       2.500000     1.400000     6.200000     0.200000     0.000000   \n",
              "50%       6.300000     2.800000     8.800000     0.500000     0.250000   \n",
              "75%      11.800000     4.700000    13.200000     0.860000     0.500000   \n",
              "max      36.300000    14.800000    68.200000     2.800000     4.000000   \n",
              "\n",
              "              TOPG  \n",
              "count  1573.000000  \n",
              "mean      0.993846  \n",
              "std       0.950863  \n",
              "min       0.000000  \n",
              "25%       0.330000  \n",
              "50%       0.750000  \n",
              "75%       1.330000  \n",
              "max       5.170000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d912f1be-ad99-4884-a2f1-cb56fa0160ce\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AGE</th>\n",
              "      <th>GP</th>\n",
              "      <th>MPG</th>\n",
              "      <th>MIN%</th>\n",
              "      <th>USG%</th>\n",
              "      <th>TO%</th>\n",
              "      <th>FTA</th>\n",
              "      <th>FT%</th>\n",
              "      <th>2PA</th>\n",
              "      <th>3PA</th>\n",
              "      <th>PPG</th>\n",
              "      <th>RPG</th>\n",
              "      <th>TRB%</th>\n",
              "      <th>SPG</th>\n",
              "      <th>BPG</th>\n",
              "      <th>TOPG</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
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              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
              "      <td>1573.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.993484</td>\n",
              "      <td>19.089002</td>\n",
              "      <td>18.794469</td>\n",
              "      <td>39.149841</td>\n",
              "      <td>18.128163</td>\n",
              "      <td>10.087659</td>\n",
              "      <td>39.746980</td>\n",
              "      <td>0.620615</td>\n",
              "      <td>95.001271</td>\n",
              "      <td>62.460267</td>\n",
              "      <td>8.172600</td>\n",
              "      <td>3.413795</td>\n",
              "      <td>10.107883</td>\n",
              "      <td>0.565105</td>\n",
              "      <td>0.358010</td>\n",
              "      <td>0.993846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.223815</td>\n",
              "      <td>20.440654</td>\n",
              "      <td>11.401266</td>\n",
              "      <td>23.756698</td>\n",
              "      <td>7.529271</td>\n",
              "      <td>8.856864</td>\n",
              "      <td>69.469204</td>\n",
              "      <td>0.325143</td>\n",
              "      <td>152.547152</td>\n",
              "      <td>103.187201</td>\n",
              "      <td>7.136968</td>\n",
              "      <td>2.693283</td>\n",
              "      <td>6.387825</td>\n",
              "      <td>0.488581</td>\n",
              "      <td>0.427527</td>\n",
              "      <td>0.950863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.400000</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",
              "      <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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.670000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>13.500000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.530000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>37.300000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.744000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>6.300000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>8.800000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29.870000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>60.400000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>14.100000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>0.842000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>11.800000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.330000</td>\n",
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              "      <td>41.780000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>41.900000</td>\n",
              "      <td>87.300000</td>\n",
              "      <td>63.700000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>691.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1238.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>36.300000</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>68.200000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.170000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Despite the results found in the histogram, the averages give very different results from the majority found previously, which indicates high values for more qualified athletes, this can also be verified due to the high values of standard deviation in 2PA and 3PA. A big regret in our data is that the median of some values approach zero, indicating that in certain cases almost half of the athletes do not perform blocks per game, for example, so, in some data, the count distribution is sparse with many values close to zero, we need to treat such data later."
      ],
      "metadata": {
        "id": "HwivShbFICIh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In addition to the numerical data, in our data we have three categoricals, to find out more information about two of them we will use the value_counts() method,"
      ],
      "metadata": {
        "id": "0Oq6STK4uaZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#chosen color map\n",
        "cmap2 = mpl.cm.Set3\n",
        "\n",
        "#list of data that we will plot\n",
        "listFirst = ['TEAM','POS']\n",
        "\n",
        "fig=plt.figure(figsize=(16,12))\n",
        "\n",
        "#loop that plots all the data we want using the value_counts() method\n",
        "for i, col in enumerate(listFirst):\n",
        "  ax=fig.add_subplot(2,2,i+1)\n",
        "  plt.xlabel('Number of Occurrences')\n",
        "  plt.ylabel('Categories')\n",
        "  valuec = NbaDataset[col].value_counts().plot(kind='barh',title=col,color=cmap2(i + 4),grid=True,ax=ax)\n",
        "  plt.subplots_adjust(left=0.125,\n",
        "                    bottom=0.1, \n",
        "                    right=1.4, \n",
        "                    top=1.35, \n",
        "                    wspace=0.4, \n",
        "                    hspace=0.35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "F_DvoDflvFRR",
        "outputId": "b49d813e-fc7a-4176-fdb5-97ba6176d7b1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "notamos que a maioria dos atletas se encontram na equipe Mil (Milwaukee Bucks) e a minoria deles pertencem à Cha (Charlotte Hornets). Além do mais, grande parte dos atletas atuam na posição de guarda G, e a minoria deles são SF Small Forward (ALA)."
      ],
      "metadata": {
        "id": "_IOUPl4FzBCt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4 - Stratified Sampling"
      ],
      "metadata": {
        "id": "wYZfhlHrE5rm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we will see later, the minutes per game of the team used by an athlete is a crucial attribute to estimate the points per game of an athlete, as we do not want biased results we will make a stratified sampling, as we will divide all athletes into smaller and uniform groups, entitled of strata. Finally, we take a fragment of the exact number of instances from each stratum, in order to ensure that the test set characterizes the athletes as a whole."
      ],
      "metadata": {
        "id": "0dYCQQwJE-zq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our first histogram the minutes per game of the team used by an athlete ranges from 0 min to 40 min approximately, so we will create a category attribute of mini minutes per game, with 5 categories labeled up to 5."
      ],
      "metadata": {
        "id": "cQJ0sg3FQlx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset[\"MPG_cat\"] = pd.cut(NbaDataset[\"MPG\"],\n",
        "                               bins=[0., 10., 20., 30., 40., np.inf],\n",
        "                               labels=[1, 2, 3, 4, 5])"
      ],
      "metadata": {
        "id": "tg4Cdng1NFxK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The minute-per-game categories used by an athlete are shown below:"
      ],
      "metadata": {
        "id": "3ieQy7llUaYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "NbaDataset[\"MPG_cat\"].hist()\n",
        "plt.xlabel('Categories of Minutes per Game')\n",
        "plt.ylabel('Frequency Density')\n",
        "plt.title('Histograms of minutes per game categories')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "31Q7Zv7cQErF",
        "outputId": "449ef910-3bc2-484f-ebf9-3687d9a676a9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "after taking subpopulations of athletes, we perform the stratification."
      ],
      "metadata": {
        "id": "cC197tvPQFDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(NbaDataset, NbaDataset[\"MPG_cat\"]):\n",
        "    strat_train_set = NbaDataset.iloc[train_index]\n",
        "    strat_test_set = NbaDataset.iloc[test_index]"
      ],
      "metadata": {
        "id": "DKv7sn0WU_OX"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To check the consistency of our stratification we make the comparison:"
      ],
      "metadata": {
        "id": "j7XjEZVel6HT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set['MPG_cat'].value_counts()/len(strat_train_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G78Afniwl5go",
        "outputId": "bece1b1c-6c1a-455d-e9e3-3afca09cdac0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.290938\n",
              "2    0.256757\n",
              "3    0.236884\n",
              "4    0.207472\n",
              "5    0.007949\n",
              "Name: MPG_cat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset['MPG_cat'].value_counts()/len(NbaDataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-d7OWjWtnC5H",
        "outputId": "a7743418-16c4-4b74-c880-b615b9f19e15"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.291163\n",
              "2    0.256834\n",
              "3    0.236491\n",
              "4    0.207247\n",
              "5    0.008264\n",
              "Name: MPG_cat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "and we noticed a consistency in the results."
      ],
      "metadata": {
        "id": "U_K9nEtyn3jp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "finally we return the data to the original state"
      ],
      "metadata": {
        "id": "hTj-f5XcrGa9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "for set_ in (strat_train_set, strat_test_set):\n",
        "    set_.drop(\"MPG_cat\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "bOqIhcroq7Ls"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5 - Visualizing data and Answering Some Business Questions"
      ],
      "metadata": {
        "id": "noRemSr-rc-9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After we've gained insights from our data, it's time to answer the business questions before moving on to the machine learning model. We would like to obtain some information about the age of the athletes, for this we will create age groups with the cut() method."
      ],
      "metadata": {
        "id": "8_p9F3UN45_A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#our bins\n",
        "classes = [17,20, 23, 26, 29, 32,35,38,40]\n",
        "\n",
        "#the labels for each class\n",
        "labels1 = ['17-20', '20-23', '23-26', '26-29', '29-32','32-35','35-38','38-40']\n",
        "\n",
        "Dataset['age_cut'] = pd.cut(Dataset[\"AGE\"], bins=classes, labels=labels1)"
      ],
      "metadata": {
        "id": "cU5m_dF-JcCI"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can start plotting the graphs. An important attribute as we will see later on is the team minutes per game used by a player (MPG). We will initially plot the MPGxPPG and we will highlight the labels to differentiate the age groups of the players,"
      ],
      "metadata": {
        "id": "o25QsXY3MXIO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#chart using seaborn\n",
        "graph = sns.relplot(x=Dataset['MPG'],y=Dataset['PPG'],hue=Dataset['age_cut'],height = 7)\n",
        "graph.set(title=\"Players' score and time on court at each age\")\n",
        "graph.set_ylabels(\"Points Per Game\", clear_inner=False)\n",
        "graph.set_xlabels(\"Minutes per Game (min)\", clear_inner=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "mVvVw7jeSK7x",
        "outputId": "ae1718ee-681b-4daf-b2d6-13c7569b41a4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f81088feb50>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 575.475x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "through this graph we can see the great assimilation of the MPGxPPG attributes, that is, the longer a player is on the court, the more points he scores. In addition, in this graph we understand that the two athletes who punctuate the most were those between 23 and 26 years old and those between 20 and 23 years old. These players punctuate these points in approximately 37-41 min. The player with the fewest points had an age range of 23 to 26 years. Despite these results, which age group pattern scores more?"
      ],
      "metadata": {
        "id": "1J_qTHE03Z8v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To answer this question let's use the groupby('variable').sum() method to group data from the same class and add that data to find out which age group, age and team punctuate the most."
      ],
      "metadata": {
        "id": "8s3utm9mZjzE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#color map we use \n",
        "cmap3 = mpl.cm.Set2\n",
        "\n",
        "#labels that will be inserted on the y-axis\n",
        "labels1 = ['17-20', '20-23', '23-26', '26-29', '29-32','32-35','35-38','38-40']\n",
        "labels2 = [team for team, Dataset in Dataset.groupby('TEAM')]\n",
        "labels3 = [pos for pos, Dataset in Dataset.groupby('POS')]\n",
        "\n",
        "#dictionary containing the labels to plot\n",
        "player_dict = {'l1':labels1,'l2':labels2,'l3':labels3}\n",
        "\n",
        "#the list that we are going to go through to find the best ponctuate for each category\n",
        "resultsList = ['age_cut','TEAM','POS']\n",
        "\n",
        "fig=plt.figure(figsize=(18,18))\n",
        "\n",
        "\n",
        "for val,i in zip(enumerate(player_dict.values()),resultsList):\n",
        "  #use the groupby('age_cut').sum() methods to group the tracks for example and sum the data of their group\n",
        "  results = Dataset.groupby(i).sum()\n",
        "  ax=fig.add_subplot(3,3,val[0]+1)\n",
        "  #we plot the vertical bar\n",
        "  ax.barh(val[1],results['PPG'],color=cmap3(val[0] + 1))\n",
        "  ax.set_ylabel(i)\n",
        "  ax.set_xlabel('points per game')\n",
        "fig.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "0H8BkaQHRMer",
        "outputId": "6678dd49-df68-498b-d333-d5e4a1e92aa7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this graph we have the classes that score the most points per game, so we understand that the age of the players who score the most points varies from 26-29 years, and those who score the least are those with 38-40 years. Similarly, the team that scores the most is the Portland Trail Blazers (POR), and the one that scores the least is the Charlotte Hornets (Cha). Finally, the positions that score the most are Guard (G), and the ones that score the least are Power forward (PF)"
      ],
      "metadata": {
        "id": "QZuO_8AGZHhY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could make the same scatter chart as before for the position of the players and for the team as well, however, the information obtained in that scatter chart was enough, since this chart does not give us statistical information, however what if we wanted to know who exactly the players are who score the most? For this we will plot the scores and assistance of the players."
      ],
      "metadata": {
        "id": "zd5a2Avm8UxL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#we caught the baddest players\n",
        "fewerdata = Dataset[(Dataset['PPG'] > 25) & (Dataset['APG'] > 2)].copy()\n",
        "\n",
        "#we plot\n",
        "graph = sns.relplot(x=fewerdata['APG'],y=fewerdata['PPG'],height = 7,hue=fewerdata['FULL NAME'],s=100,marker=r'D')\n",
        "graph.set(title=\"Player points and their assists\")\n",
        "graph.set_ylabels(\"Points Per Game\", clear_inner=False)\n",
        "graph.set_xlabels(\"Assistance per game\", clear_inner=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "GkWXCxmeLSLA",
        "outputId": "2ae5f596-03e5-408f-bb5b-a023b527c4d3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f8108ff1890>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 676.35x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The athlete with the highest score is Donovan Mitchell, and the athlete with the most assists and the second highest number of points is Luka Doncic. There are other athletes who have more assistance, but they didn't score more than 25 points per game, so they don't appear on the chart as is the case with Rajon Rondo. Furthermore, some players appear more than once because in our data we have several seasons in which the same player may have participated."
      ],
      "metadata": {
        "id": "RW8cnJh8frj1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we've already plotted the assists before, we can see similarly to the previous graphs, which are the age groups, teams and positions of the players who perform the most assists."
      ],
      "metadata": {
        "id": "VWFvEmfXKIuM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The following chart has the same structure as the previous ones\n",
        "player_dict = {'l1':labels1,'l2':labels2,'l3':labels3}\n",
        "\n",
        "resultsList = ['age_cut','TEAM','POS']\n",
        "\n",
        "fig=plt.figure(figsize=(18,18))\n",
        "\n",
        "\n",
        "for val,i in zip(enumerate(player_dict.values()),resultsList):\n",
        "  results = Dataset.groupby(i).sum()\n",
        "  ax=fig.add_subplot(3,3,val[0]+1)\n",
        "\n",
        "  ax.barh(val[1],results['APG'],color=cmap3(val[0]/(val[0] + 2)))\n",
        "\n",
        "  ax.set_ylabel(i)\n",
        "  ax.set_xlabel('assists per game')\n",
        "fig.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "kjyBKDvb5C1D",
        "outputId": "6d1c10f6-f667-4056-b7bf-34555b163d08"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once again, the players who assist the most are 26-29 years old, and the least assists are 38-40 years old. Similar to the previous chart, the team that has the most assists is the Milwaukee Bucks (MIL), the one that does the least is the Chicago Bulls (Chi). Finally, the psition that most assists is guard (G), and the one that least assists is power forward (PF)."
      ],
      "metadata": {
        "id": "V58Aq6ao6Bhm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Despite the similarity between the graphs of the previous cells, we can easily verify that the results are different from both graphs."
      ],
      "metadata": {
        "id": "SgpUAwex9cMI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = Dataset.groupby('age_cut').sum()\n",
        "print(max(results['PPG']),max(results['APG']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NjNN6BV89Ks",
        "outputId": "ef204cc2-ef00-4eef-f690-01c0bb8fc134"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3266.5 689.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One relevant thing that we will see later on is the relevance of the TOPG attribute, which is the number of turnovers per game. In basketball, a turnover occurs when a team loses possession of the ball to the opposing team before a player tries to hit his team's basket, increasing the chance of the team that performed the turnover, scoring a point, hence its importance."
      ],
      "metadata": {
        "id": "TQSVqlWC94GF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find out who are the players who most turnover and points per game, we will plot again the graph that appears to be a scatter."
      ],
      "metadata": {
        "id": "R_U1i-1QX8iT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#caught the best players\n",
        "morefewerdata = Dataset[(Dataset['PPG'] > 25) & (Dataset['TOPG'] > 2)].copy()\n",
        "\n",
        "#plot\n",
        "graph = sns.relplot(x=morefewerdata['TOPG'],y=morefewerdata['PPG'],height = 7,hue=morefewerdata['FULL NAME'],s=100,marker=r's')\n",
        "graph.set(title=\"Player points and their turnovers\")\n",
        "graph.set_ylabels(\"Points Per Game\", clear_inner=False)\n",
        "graph.set_xlabels(\"Turnovers\", clear_inner=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "yxi8IwMTYfD3",
        "outputId": "3b4f0217-e8ea-461c-8503-a67cfc55fb01"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f810c4b57d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 676.35x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The players who scored the most points we already know, but the one who simultaneously made more turnover and more points on this graph was Luka Doncic."
      ],
      "metadata": {
        "id": "xCtBS6XaiIRu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we would like to know which age groups, teams and positions of players perform the most turnovers."
      ],
      "metadata": {
        "id": "_1C5_n-eBtU_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cmap4 = mpl.cm.Accent\n",
        "\n",
        "fig=plt.figure(figsize=(18,18))\n",
        "\n",
        "\n",
        "for val,i in zip(enumerate(player_dict.values()),resultsList):\n",
        "  results = Dataset.groupby(i).sum()\n",
        "  ax=fig.add_subplot(3,3,val[0]+1)\n",
        "\n",
        "  ax.barh(val[1],results['TOPG'],color=cmap4(val[0]/(val[0] + 4)))\n",
        "\n",
        "  ax.set_ylabel(i)\n",
        "  ax.set_xlabel('turnover per game')\n",
        "fig.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "k7_NGM4dBvHW",
        "outputId": "d1898793-9cdb-468f-bcc2-1cd3d7664853"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the first graph we have the same result obtained previously, in the second graph we find that the team that performed the most turnover was the Portland Trail Blazers (Por), and the team that performed the most turnover was the Charlotte Hornets (Cha). In the last graph, the position with the highest turnover was Guard (G), and the one with the least turnover was the power forward (PF)."
      ],
      "metadata": {
        "id": "LAL8pzuXCPVk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could spend more time here checking other attributes, however, as the model's proposal is to predict the points per game of an athlete, we focus more on the variables most related to this target value, which we will see later because with the correlation matrix ."
      ],
      "metadata": {
        "id": "tTjNCR2xlLah"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6 - Exploratory Data Analysis and Feature Engineering"
      ],
      "metadata": {
        "id": "3u50vBCK5MeE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you may have seen we were working in some cases with the 'Dataset' dataset I did this, because I think the 'Dataset' attribute Per Game Assistance (APG) is not a legitimate attribute, so just in case I created two sets of data. From now on we will use the 'NbaDataset' dataset which does not have such an attribute."
      ],
      "metadata": {
        "id": "nMk9Oabf5P3L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1 - Creating New Data\n"
      ],
      "metadata": {
        "id": "KCnA7eYf6Nre"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will test combinations of features to create new data. Initially, we will take a copy of our training data."
      ],
      "metadata": {
        "id": "4JG3nERe6Qrp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset = strat_train_set.copy()"
      ],
      "metadata": {
        "id": "dvtOow6R66Zy"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To create the new attributes we will do the following, we have several data that were created for each game, basically we will multiply these values by the number of games that players played. Furthermore, the data that was not created for each game will also be created, that is, we will divide this data by the games played attribute."
      ],
      "metadata": {
        "id": "K1Bj0vCp8brc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset['MT'] = NbaDataset['MPG']*NbaDataset['GP'] #create the total minutes of a player on court\n",
        "NbaDataset['FTAPG'] = NbaDataset['FTA']/NbaDataset['GP'] # free throw attempts per game\n",
        "NbaDataset['2PAPG'] = NbaDataset['2PA']/NbaDataset['GP'] #attempts to fild goals of two points per match\n",
        "NbaDataset['3PAPG'] = NbaDataset['3PA']/NbaDataset['GP'] #attempts to fild goals of 3 points per match\n",
        "NbaDataset['RT'] = NbaDataset['RPG']*NbaDataset['GP'] #total rebounds\n",
        "NbaDataset['RT'] = NbaDataset['SPG']*NbaDataset['GP'] #total robberies\n",
        "NbaDataset['BT'] = NbaDataset['BPG']*NbaDataset['GP'] #total blocks\n",
        "NbaDataset['TOT'] = NbaDataset['TOPG']*NbaDataset['GP'] # Turn Over Total"
      ],
      "metadata": {
        "id": "aH-1SYnt9XkS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.2 Search for Correlations\n"
      ],
      "metadata": {
        "id": "zoP87DrjBvgq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now one of the most important parts of the project has arrived, because we are going to try to understand how the attributes of our dataset relate to each other, for this we will use the correlation coefficient through the corr() method."
      ],
      "metadata": {
        "id": "rc7OVWok_1Qt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Matrix_corr = NbaDataset.corr()\n",
        "Matrix_corr['PPG'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNiaHpKJDUp4",
        "outputId": "d2c61934-3770-4c8f-8f21-da81e3f7bd9c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PPG      1.000000\n",
              "2PAPG    0.921830\n",
              "MIN%     0.878182\n",
              "MPG      0.878172\n",
              "FTAPG    0.862689\n",
              "TOPG     0.831177\n",
              "3PAPG    0.755774\n",
              "RPG      0.691177\n",
              "SPG      0.641844\n",
              "FTA      0.578424\n",
              "2PA      0.573604\n",
              "USG%     0.542443\n",
              "TOT      0.538108\n",
              "FT%      0.472791\n",
              "MT       0.470625\n",
              "3PA      0.463182\n",
              "RT       0.422013\n",
              "BPG      0.399867\n",
              "BT       0.305581\n",
              "GP       0.268209\n",
              "AGE      0.106283\n",
              "TRB%     0.002345\n",
              "TO%     -0.047969\n",
              "Name: PPG, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The correlation coefficient close to 1 reveals a positive relationship between the data, if the correlation coefficient is close to -1 it expresses a negative relationship. For example, a player's points tend to increase the more he tries to hit 2-point fild goals, the points per game a player scores tend to increase as TO% decreases (number of turnover a player commits per 100 possessions) note that It's not the TOPG. Now we know why we stratified our data into five categories of minutes per game used by a player on court (MPG), as the relationship with the target value is high, around 0.87."
      ],
      "metadata": {
        "id": "OvGX3e_ZGwKL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Furthermore, we can look at these relationships in the form of a heat map."
      ],
      "metadata": {
        "id": "D2oN8cXROKNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#we avoid a repetition of the correlation map with mask\n",
        "mask = np.triu(np.ones_like(Matrix_corr))\n",
        "\n",
        "f=plt.figure(figsize=(12,8))\n",
        "\n",
        "#we create the heat map\n",
        "graph = sns.heatmap(Matrix_corr,mask=mask,cmap=plt.get_cmap(\"YlGn\"),center=0,square=True,linewidths=.5,cbar_kws={\"shrink\": .5})\n",
        "graph.set_title(\"Correlation Matrix Heat Map\",fontsize = 20)\n",
        "sns.set(font_scale = 1)\n",
        "f.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "dXQUhcdZKcMB",
        "outputId": "fb50033d-d690-4b1c-dc09-802bfc3677d8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most impressive in our attribute correlation heatmap is the relationship between PA2PG (2 points per game) and PPG (points per game), as we also see again the strong relationship between PPG and MPG."
      ],
      "metadata": {
        "id": "00WM5sNEVE0t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.3 Prepare the Data"
      ],
      "metadata": {
        "id": "qUMXFCs4cDAt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we will return to our dataset without any changes, and we will split the labels and predictors as we cannot impute transformations on the labels."
      ],
      "metadata": {
        "id": "so733LRDdzS8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset = strat_train_set.drop(columns=['PPG','FULL NAME'], axis=1) # drop labels for training set\n",
        "Nba_labels = strat_train_set['PPG'].copy()\n",
        "\n",
        "NbaDataset = NbaDataset.astype({'GP':'float','FTA':'float','2PA':'float','3PA':'float'})\n",
        "\n",
        "Nba_num = NbaDataset.select_dtypes(include=[np.number])#only the numerical data for later"
      ],
      "metadata": {
        "id": "4i2Gxc26cZIw"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.4 - Creating our own transformer"
      ],
      "metadata": {
        "id": "7ktQDvlsWFQ_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We already know what our attributes will be created through combinations, however we must create a standard transformer to incorporate our combined attributes into the dataset when we are going to use the pipeline transformation. A pepiline transformation sequentially employs several estimates and transformations through fit_transform() on the data. In other words, we must do this, because our pipeline needs to have a fit() and transform(), which was not done in our usual creation of previous data."
      ],
      "metadata": {
        "id": "eQ9SiMk8N2Vh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin # the TransformerMixin to ensure fit_transform()\n",
        "\n",
        "#we must first get the column indices\n",
        "names = \"GP\", \"FTA\", \"2PA\", \"3PA\",\"TOPG\"\n",
        "gp_ix, fta_ix, pa2_ix, pa3_ix, topg_ix = [Nba_num.columns.get_loc(i) for i in names ] \n",
        "\n",
        "#create the transformer that will imput the fit_transform\n",
        "class CreateCombinedAttributes(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, add_PA2PG=True,add_FTAPG=True,add_PA3PG=True): \n",
        "        self.add_PA2PG = add_PA2PG\n",
        "        self.add_FTAPG = add_FTAPG\n",
        "        self.add_PA3PG = add_PA3PG\n",
        "    def fit(self, X, y=None):\n",
        "        return self #to implement the fit method\n",
        "    def transform(self, X): \n",
        "        TOT = X[:, topg_ix]*X[:, gp_ix]\n",
        "        if (self.add_PA2PG & self.add_FTAPG & self.add_PA3PG):\n",
        "            PA2PG = X[:, pa2_ix] / X[:, gp_ix]\n",
        "            FTAPG = X[:, fta_ix] / X[:, gp_ix]\n",
        "            PA3PG = X[:, pa3_ix] / X[:, gp_ix]\n",
        "            return np.c_[X, FTAPG, PA3PG,\n",
        "                         PA2PG,TOT]\n",
        "        else:\n",
        "            return np.c_[X, TOT]"
      ],
      "metadata": {
        "id": "U-MuIE_ZfIFd"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We don't all create the same resources as before, as some of them are not that important. the initial setting add_PA2PG=True will tell us if the PA2PG parameter is really relevant as well as the others."
      ],
      "metadata": {
        "id": "uRZFiGdYX2Zv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.5 - Transformations by Pipelines (Cleaning, scaling, etc...)"
      ],
      "metadata": {
        "id": "UDqDwqHkkS7r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our dataset, some attributes have null values:"
      ],
      "metadata": {
        "id": "mWbZliTAlSD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isnull(NbaDataset).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0uxELCWk3ET",
        "outputId": "b581192c-9512-4106-b10f-1c082391aa6b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TEAM     0\n",
              "POS      0\n",
              "AGE      0\n",
              "GP       0\n",
              "MPG      0\n",
              "MIN%     0\n",
              "USG%     0\n",
              "TO%     36\n",
              "FTA      0\n",
              "FT%      0\n",
              "2PA      0\n",
              "3PA      0\n",
              "RPG      0\n",
              "TRB%     0\n",
              "SPG      0\n",
              "BPG      0\n",
              "TOPG     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As our data has many imperfections we must remedy them by employing a pepilines transformation, which sequentially applies various estimates and transformations to the data through the fit_transform() method. In our pipeline:\n",
        "\n",
        "1. First, we fill in the missing data in the dataset, with the column's median through the SimpleImputer(strategy='median') method;\n",
        "2. Then with our CreateCombinedAttributes() method we create the combined attributes;\n",
        "3. We apply the PowerTransformer() method which employs Loarithmic and power transformations to improve normality or symmetry in the data. We do this because in our first two plots (of section 4.3) we have those long tails in the histograms, and this method generates more Gauss-like data.\n",
        "4. Finally, we use the MinMaxScaler(), which makes the data have similar weights, so that this normalization induces our data to be in a range from 0 to 1."
      ],
      "metadata": {
        "id": "BaYjSHkRkQ95"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Such transformations in our numeric attributes\n",
        "\n",
        "from imblearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import MinMaxScaler,PowerTransformer\n",
        "\n",
        "#pipeline that enforces the transformations\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', SimpleImputer(strategy='median')),\n",
        "        ('crate_attributes', CreateCombinedAttributes()),\n",
        "        ('PowerTransformer',PowerTransformer(method='yeo-johnson',standardize=True)),\n",
        "        ('MinMaxScaler',MinMaxScaler()),\n",
        "          \n",
        "    ])\n",
        "\n",
        "dataset_num_tr = num_pipeline.fit_transform(Nba_num)"
      ],
      "metadata": {
        "id": "tQertnyJiyFw"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we join our numerical and categorical data through the ColumnTransformer and use the OneHotEncoder method, to transform words into numbers, as the models do not work with words, only numbers."
      ],
      "metadata": {
        "id": "ruM6F-yequ4w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "#list the numerical data\n",
        "num_attribs = list(Nba_num)\n",
        "\n",
        "\n",
        "dataset_cat = NbaDataset.select_dtypes(include=['object'])#take the categorical data\n",
        "cat_attribs = list(dataset_cat)#list the categorical data\n",
        "\n",
        "#apply the total transformation to the categorical and numerical data\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_attribs),\n",
        "        (\"cat\", OneHotEncoder(handle_unknown = 'ignore'), cat_attribs)\n",
        "    ])\n",
        "\n",
        "\n",
        "\n",
        "dataset_prepared = full_pipeline.fit_transform(NbaDataset)"
      ],
      "metadata": {
        "id": "LczB1k9ejlEV"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7 - Choose and Train The Model"
      ],
      "metadata": {
        "id": "pJ5KHwXBZBcw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this step, we will analyze several models in order to find the best one for our project, as our dataset is not very large, we will"
      ],
      "metadata": {
        "id": "0cK499SOZayO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Regression based on k-nearest neighbors\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "knn = KNeighborsRegressor().fit(dataset_prepared, Nba_labels)\n",
        "scoreknn = knn.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Linear Regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "linreg = LinearRegression().fit(dataset_prepared, Nba_labels)\n",
        "scoreline = linreg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Ridge Regression\n",
        "from sklearn.linear_model import Ridge\n",
        "RidgeReg = Ridge(alpha=1,solver='sag',random_state=42).fit(dataset_prepared, Nba_labels)\n",
        "scoreRidge = RidgeReg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Lasso Regression\n",
        "from sklearn.linear_model import Lasso\n",
        "LassoReg = Lasso(alpha=0.001,random_state=42,max_iter = 10000).fit(dataset_prepared, Nba_labels)\n",
        "scoreLasso = LassoReg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Epsilon-Support Vector Regression\n",
        "from sklearn.svm import SVR\n",
        "SVRreg = SVR(kernel = 'rbf').fit(dataset_prepared, Nba_labels)\n",
        "scoreSVR = SVRreg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Decision Tree Regressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "DecisionReg = DecisionTreeRegressor(min_samples_split=20,random_state=42).fit(dataset_prepared, Nba_labels)\n",
        "scoreDtree = DecisionReg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "RFreg  = RandomForestRegressor(random_state=42).fit(dataset_prepared, Nba_labels)\n",
        "scoreRFreg = RFreg.score(dataset_prepared, Nba_labels)\n",
        "\n",
        "\n",
        "#Extreme Gradient Boosting for regression\n",
        "from xgboost import XGBRegressor\n",
        "XGBreg = XGBRegressor(objective ='reg:squarederror',random_state=42).fit(dataset_prepared, Nba_labels)\n",
        "scoreXGBreg = XGBreg.score(dataset_prepared, Nba_labels)\n"
      ],
      "metadata": {
        "id": "GfAjqVgta7px"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.1 - Finding the best model"
      ],
      "metadata": {
        "id": "HHkNrHLwjhjE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will plot the scores of each model:"
      ],
      "metadata": {
        "id": "A9LX05RxCuz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "names = ['KnnReg','linreg','RidgeReg','LassoReg','SVRreg','DecisionReg','RFreg','XGBreg']\n",
        "values = [scoreknn,scoreline,scoreRidge,scoreLasso,scoreSVR,scoreDtree,scoreRFreg,scoreXGBreg]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(11, 9))\n",
        "  \n",
        "# plot the data\n",
        "ax.plot(names, values,lw=3,color = 'teal')\n",
        "ax.set_title('Analyzing The Scores Of the Models',fontsize=18)\n",
        "ax.set_xlabel('Models',fontsize=17)\n",
        "ax.set_ylabel('Model Score',fontsize=17)\n",
        "  \n",
        "# change the fontsize\n",
        "ax.tick_params(axis='both', which='minor', labelsize=8)\n",
        "ax.tick_params(axis='both', which='major', labelsize=14)\n",
        "\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "tvCeSA9sjrsn",
        "outputId": "7fa929ad-f393-45df-a8f4-21263e8320b2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that the best models are decision treen, Random Forest Regressor and Extreme Gradient Boosting for regression, so we will expose them here,"
      ],
      "metadata": {
        "id": "TRhUBluYsWiI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Random Forest Regressor Score:', scoreRFreg, ', Extreme Gradient Boost Score for Regression:',scoreXGBreg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o28nErGqsge2",
        "outputId": "82446455-5199-4628-82a6-2c593d7c8ba8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Regressor Score: 0.99517319110807 , Extreme Gradient Boost Score for Regression: 0.9875431617037801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "but the winner can occur the Extreme Gradient Boost regression, as the Rand Forest Regressor can occur an adjustment."
      ],
      "metadata": {
        "id": "LqdRWfaAuFO2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extreme Gradient Boosting is a model that has perfected Gradient Boosting, so to understand XGBRegressor we must first know how Gradient boosting works. Basically Gradient Boosting sequentially embeds predictors into a set (these sets are built using decision tree models), all of which correct their previous predictor. This operation causes the new predictors to be adapted based on the residual error performed by the preceding predictor. Extreme Gradient Boost enjoys \"a more regularized model formalization to control over-fitting, which gives it better performance\" (Tianqi Chen)\n",
        "\n"
      ],
      "metadata": {
        "id": "RAHWeDs0EHlO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For one more evaluation of the model, we used the Mean Square Error, which measures the mean squared difference between the recorded values and the real value."
      ],
      "metadata": {
        "id": "eicxL54yjtk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "\n",
        "dataset_predictions = XGBreg.predict(dataset_prepared)\n",
        "lin_mse = mean_squared_error(Nba_labels, dataset_predictions)\n",
        "lin_rmse = np.sqrt(lin_mse)\n",
        "lin_rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAptq4FPjzMM",
        "outputId": "87d53025-dd93-462c-fa28-b7f04d55ec95"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8032056502908369"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "our model is good, because the closer to zero the better."
      ],
      "metadata": {
        "id": "IR1pdG_IxZBP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 - Using cross validation"
      ],
      "metadata": {
        "id": "vuyRTSA9oWmw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will divide the training set into 10 different subgroups called folds, after this procedure the method evaluates the Extreme Gradient Boost model 10 times, each time selecting a different fold for training and evaluation among the 9 folds."
      ],
      "metadata": {
        "id": "uQ5HwTU0pGRV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(XGBreg, dataset_prepared, Nba_labels, cv=10)\n"
      ],
      "metadata": {
        "id": "7mb-VdksqmXn"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def display_scores(scores):\n",
        "    print(\"\\n Scores:\", scores)\n",
        "    print(\"\\n Standard deviation:\", scores.std())\n",
        "    print(\"\\n Mean:\", scores.mean())\n",
        "\n",
        "display_scores(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJp1rLcw3K5M",
        "outputId": "6061983b-73b3-48a4-a0a4-59d4838d2f69"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Scores: [0.97010431 0.94843396 0.96445295 0.97379446 0.97651405 0.96611678\n",
            " 0.96349309 0.97268869 0.96668437 0.97153209]\n",
            "\n",
            " Standard deviation: 0.007497518002143043\n",
            "\n",
            " Mean: 0.9673814747864501\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model looks like everything is ok."
      ],
      "metadata": {
        "id": "9DlwNERy1xHZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8 - Perfecting the Model"
      ],
      "metadata": {
        "id": "LO37eBNJFsL8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we know the model is good, let's see if we can improve it further."
      ],
      "metadata": {
        "id": "Z-MuzipgdgvP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.1 - Randomized Search"
      ],
      "metadata": {
        "id": "dmeN5lDwdu7l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will look for several hyperparameters to be tested in our model using cross-validation. For this we will use the random search."
      ],
      "metadata": {
        "id": "kvaBctT4d6ab"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import randint\n",
        "from xgboost import XGBRegressor\n",
        "\n",
        "xgb = XGBRegressor()\n",
        "parameters = {\n",
        "              'objective':['reg:linear','reg:squarederror','reg:gamma','reg:tweedie'],\n",
        "              'learning_rate': [.03, 0.05, .07 , .01], #so called `eta` value\n",
        "              'max_depth': [5, 6, 7,8,9],\n",
        "              'min_child_weight': [2,4,6,8],\n",
        "              'silent': [1],\n",
        "              'subsample': [0.7],\n",
        "              'colsample_bytree': [0.7],\n",
        "              'n_estimators': [3, 10, 30,100]\n",
        "              ,'max_features': [2,5,8,14,20]}\n",
        "\n",
        "xgb_grid =  RandomizedSearchCV(xgb, param_distributions=parameters,\n",
        "                                n_iter=10, cv=5, random_state=42)\n",
        "\n",
        "\n",
        "xgb_grid.fit(dataset_prepared, Nba_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTgg541AtBEI",
        "outputId": "21702f11-5c94-4b0a-ca3f-d9b8825503d0"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=XGBRegressor(),\n",
              "                   param_distributions={'colsample_bytree': [0.7],\n",
              "                                        'learning_rate': [0.03, 0.05, 0.07,\n",
              "                                                          0.01],\n",
              "                                        'max_depth': [5, 6, 7, 8, 9],\n",
              "                                        'max_features': [2, 5, 8, 14, 20],\n",
              "                                        'min_child_weight': [2, 4, 6, 8],\n",
              "                                        'n_estimators': [3, 10, 30, 100],\n",
              "                                        'objective': ['reg:linear',\n",
              "                                                      'reg:squarederror',\n",
              "                                                      'reg:gamma',\n",
              "                                                      'reg:tweedie'],\n",
              "                                        'silent': [1], 'subsample': [0.7]},\n",
              "                   random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "with that the best hyperparameters,"
      ],
      "metadata": {
        "id": "iG-4ezckecAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_grid.best_params_"
      ],
      "metadata": {
        "id": "tQzkKxlStYCh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b69c0071-2470-4207-e911-27e8a9be6182"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.7,\n",
              " 'learning_rate': 0.07,\n",
              " 'max_depth': 6,\n",
              " 'max_features': 14,\n",
              " 'min_child_weight': 8,\n",
              " 'n_estimators': 100,\n",
              " 'objective': 'reg:linear',\n",
              " 'silent': 1,\n",
              " 'subsample': 0.7}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "and the best estimators are."
      ],
      "metadata": {
        "id": "5IOghU9HetEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkOMMnwmeQqf",
        "outputId": "c42ea56a-9a9f-46ed-d310-f57a3a9379d1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(colsample_bytree=0.7, learning_rate=0.07, max_depth=6,\n",
              "             max_features=14, min_child_weight=8, silent=1, subsample=0.7)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In addition we can find the best features."
      ],
      "metadata": {
        "id": "DJDRK73NeyXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances = xgb_grid.best_estimator_.feature_importances_"
      ],
      "metadata": {
        "id": "w3V6M7bj_39z"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extra_attribs = [\"FTAPG\", \"PA3PG\",\"TOT\",\"PA2PG\"]\n",
        "attributes = num_attribs + extra_attribs \n",
        "\n",
        "sorted(zip(feature_importances, attributes), reverse=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLjpu-TZuMKM",
        "outputId": "0753bb6c-c724-40bf-c43e-1ff6545f7815"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.37176296, 'MIN%'),\n",
              " (0.28317577, 'TOT'),\n",
              " (0.18125382, 'MPG'),\n",
              " (0.06998328, 'USG%'),\n",
              " (0.01870528, 'FTAPG'),\n",
              " (0.016975604, 'PA3PG'),\n",
              " (0.003418861, '3PA'),\n",
              " (0.0033419393, '2PA'),\n",
              " (0.0030465147, 'FTA'),\n",
              " (0.0025537063, 'FT%'),\n",
              " (0.0023837043, 'TOPG'),\n",
              " (0.0021429434, 'BPG'),\n",
              " (0.002090025, 'RPG'),\n",
              " (0.002024364, 'TO%'),\n",
              " (0.0019787478, 'GP'),\n",
              " (0.0017267463, 'AGE'),\n",
              " (0.001712283, 'SPG'),\n",
              " (0.0016680682, 'TRB%'),\n",
              " (0.0014511782, 'PA2PG')]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this result we will discard some features."
      ],
      "metadata": {
        "id": "3kdylVe2fCIV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NbaDataset = strat_train_set[['MIN%','MPG','TOPG','GP','USG%','FTA','2PA','3PA','BPG']].copy()\n",
        "Nba_labels = strat_train_set['PPG'].copy()\n",
        "\n",
        "NbaDataset = NbaDataset.astype({'GP':'float','FTA':'float','2PA':'float','3PA':'float'})\n",
        "\n",
        "\n",
        "Nba_num = NbaDataset.select_dtypes(include=[np.number])#only the numerical data"
      ],
      "metadata": {
        "id": "7CGHBAZX2uT7"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2 - New transformer customization"
      ],
      "metadata": {
        "id": "xFbS8jD0jsgx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also created a new transformer as we shrunk the data."
      ],
      "metadata": {
        "id": "kHLUmQITjKHs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin # the TransformerMixin to ensure fit_transform()\n",
        "\n",
        "#we must first get the column indices\n",
        "names = \"GP\", \"FTA\", \"2PA\", \"3PA\",\"TOPG\"\n",
        "gp_ix, fta_ix, pa2_ix, pa3_ix, topg_ix = [Nba_num.columns.get_loc(i) for i in names ] \n",
        "\n",
        "#create the transformer that will imput the fit_transform\n",
        "class CreateCombinedAttributes(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, add_PA2PG=True,add_FTAPG=True,add_PA3PG=True): \n",
        "        self.add_PA2PG = add_PA2PG\n",
        "        self.add_FTAPG = add_FTAPG\n",
        "        self.add_PA3PG = add_PA3PG\n",
        "    def fit(self, X, y=None):\n",
        "        return self #to implement the fit method\n",
        "    def transform(self, X): \n",
        "        TOT = X[:, topg_ix]*X[:, gp_ix]\n",
        "        if (self.add_PA2PG & self.add_FTAPG & self.add_PA3PG):\n",
        "            PA2PG = X[:, pa2_ix] / X[:, gp_ix]\n",
        "            FTAPG = X[:, fta_ix] / X[:, gp_ix]\n",
        "            PA3PG = X[:, pa3_ix] / X[:, gp_ix]\n",
        "            return np.c_[X, FTAPG, PA3PG,\n",
        "                         PA2PG,TOT]\n",
        "        else:\n",
        "            return np.c_[X, TOT]"
      ],
      "metadata": {
        "id": "7_T9OXplI6rC"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.3 -  New Pipeline"
      ],
      "metadata": {
        "id": "Sn4Fw_7_jRZi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we don't need the categorical attributes anymore."
      ],
      "metadata": {
        "id": "JQGHPqQsj5nZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#let's apply such transformations to our numeric attributes\n",
        "from imblearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import MinMaxScaler,PowerTransformer\n",
        "\n",
        "#pipeline that enforces the transformations\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', SimpleImputer(strategy='median')),\n",
        "        ('crate_attributes', CreateCombinedAttributes()),\n",
        "        ('PowerTransformer',PowerTransformer(method='yeo-johnson',standardize=True)),\n",
        "        ('MinMaxScaler',MinMaxScaler()),\n",
        "          \n",
        "    ])\n",
        "\n",
        "dataset_num_tr = num_pipeline.fit_transform(Nba_num)"
      ],
      "metadata": {
        "id": "AJp00XCFAn_k"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Despite the random search informing those best parameters, it seems that this model overfits."
      ],
      "metadata": {
        "id": "K7tPp4czgNnC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XGBreg = XGBRegressor(colsample_bytree=0.7, learning_rate=0.07, max_depth=6,\n",
        "             max_features=14, min_child_weight=8, silent=1, subsample=0.7,\n",
        "             objective ='reg:squarederror',random_state=42)\n",
        "\n",
        "XGBreg.fit(dataset_num_tr, Nba_labels)\n",
        "\n",
        "scoreXGBreg = XGBreg.score(dataset_num_tr, Nba_labels)\n",
        "print(scoreXGBreg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HG5CII__hAWj",
        "outputId": "b7f69d5c-bf5f-426b-c87d-f97df07137e8"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9917399724132091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, the best thing to do is to use the most elementary model."
      ],
      "metadata": {
        "id": "AmopLhE7hthu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "XGBreg = XGBRegressor(objective ='reg:squarederror',random_state=42).fit(dataset_prepared, Nba_labels)\n",
        "\n",
        "XGBreg.fit(dataset_num_tr, Nba_labels)\n",
        "\n",
        "scoreXGBreg = XGBreg.score(dataset_num_tr, Nba_labels)\n",
        "print(scoreXGBreg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odCxjX3D7ZLg",
        "outputId": "cc257205-5338-4239-b893-d018031d83eb"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9863787059657771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9 - Implementation in Test Set"
      ],
      "metadata": {
        "id": "jj8lizwgoNjZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we do the same procedure as before, only now in our test set."
      ],
      "metadata": {
        "id": "d8RZlq9lkZ6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_test = strat_test_set[['MIN%','MPG','TOPG','GP','USG%','FTA','2PA','3PA','BPG']].copy()\n",
        "y_test = strat_test_set[\"PPG\"].copy()\n",
        "\n",
        "X_test = X_test.astype({'GP':'float','FTA':'float','2PA':'float','3PA':'float'})\n"
      ],
      "metadata": {
        "id": "HRb9Ta0ZyzIe"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "\n",
        "X_test_prepared = num_pipeline.transform(X_test)\n",
        "\n",
        "final_predictions = XGBreg.predict(X_test_prepared)\n",
        "\n",
        "final_mse = mean_squared_error(y_test, final_predictions)\n",
        "\n",
        "final_rmse = np.sqrt(final_mse)\n"
      ],
      "metadata": {
        "id": "SZ0Mn-FUxsm9"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4eccd6c3-88b7-47ac-c1db-195f0513883d",
        "id": "Vbwsfn4axsm-"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1756553450356846"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(XGBreg.score(X_test_prepared,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "169cd017-f7bf-42ab-d015-993160fb6aa1",
        "id": "dqIHTgnKxsnA"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9708019567909331\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model is very good!"
      ],
      "metadata": {
        "id": "IqqR7ktNkcOe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10 - To Deploy"
      ],
      "metadata": {
        "id": "DsUV0gyLp2pC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's save the model, to deploy it later."
      ],
      "metadata": {
        "id": "LIoN-muXqA4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "ifzhfJykutlK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.1 Final Pipeline"
      ],
      "metadata": {
        "id": "0envnv7ysiqB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we do the final pipeline"
      ],
      "metadata": {
        "id": "KfTQECYijzBx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "num_attribs = list(Nba_num)\n",
        "\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_attribs),\n",
        "    ])\n",
        "\n",
        "\n",
        "full_pipeline_with_predictor = Pipeline([\n",
        "        (\"preparation\", full_pipeline),\n",
        "        (\"xbreg\", XGBRegressor(objective ='reg:squarederror',random_state=42).fit(dataset_prepared, Nba_labels))\n",
        "    ])\n",
        "\n",
        "full_pipeline_with_predictor.fit(NbaDataset,Nba_labels)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMmjqDOZkAj_",
        "outputId": "ff47ed49-28bd-4c03-efdb-ceb2a4f0a837"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preparation',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('crate_attributes',\n",
              "                                                                   CreateCombinedAttributes()),\n",
              "                                                                  ('PowerTransformer',\n",
              "                                                                   PowerTransformer()),\n",
              "                                                                  ('MinMaxScaler',\n",
              "                                                                   MinMaxScaler())]),\n",
              "                                                  ['MIN%', 'MPG', 'TOPG', 'GP',\n",
              "                                                   'USG%', 'FTA', '2PA', '3PA',\n",
              "                                                   'BPG'])])),\n",
              "                ('xbreg',\n",
              "                 XGBRegressor(objective='reg:squarederror', random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.2 - Saving the trained model"
      ],
      "metadata": {
        "id": "MOdBcqxCp5-j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use a dump method, which takes the clf model as an input and writes our classifier in binary to the file \"wb\"."
      ],
      "metadata": {
        "id": "rBA1vzN699Ol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = full_pipeline_with_predictor\n",
        "\n",
        "\n",
        "filename = 'trained_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "FYHggfd39qq_"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "test the model"
      ],
      "metadata": {
        "id": "VkRkIUYNsR6-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "some_data = NbaDataset.iloc[:4]\n",
        "\n",
        "some_labels = Nba_labels.iloc[:4]\n",
        "\n",
        "loadModel = pickle.load(open(filename, 'rb'))\n",
        "\n",
        "print(\"Predictions:\\t\", model.predict(some_data))\n",
        "\n",
        "print(\"Labels:\\t\\t\", list(some_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4mheIrMrAlJ",
        "outputId": "a051e691-e062-497f-ef7c-16d9502d054a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions:\t [ 4.8294353  8.338118   2.5872128 13.113668 ]\n",
            "Labels:\t\t [5.0, 8.9, 3.3, 13.2]\n"
          ]
        }
      ]
    }
  ]
}