Curry_NBA_Predictions_S.ipynb

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        "<a href=\"https://colab.research.google.com/github/kvinne-anc/Regression-Models/blob/main/Curry_NBA_Predictions_S.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "_Lambda School Data Science, Unit 2_\n",
        " \n",
        "# Sprint Challenge: Predict Steph Curry's shots 🏀\n",
        "\n",
        "For your Sprint Challenge, you'll use a dataset with all Steph Curry's NBA field goal attempts. (Regular season and playoff games, from October 28, 2009, through June 5, 2019.) \n",
        "\n",
        "You'll predict whether each shot was made, using information about the shot and the game. This is hard to predict! Try to get above 60% accuracy. The dataset was collected with the [nba_api](https://github.com/swar/nba_api) Python library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nw3CL7TE7tNq"
      },
      "source": [
        "%%capture\n",
        "import sys\n",
        "\n",
        "if 'google.colab' in sys.modules:\n",
        "    # Install packages in Colab\n",
        "    !pip install category_encoders==2.*\n",
        "    !pip install pandas-profiling==2.*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Nm24pCHy_Bo"
      },
      "source": [
        "# Read data\n",
        "import pandas as pd\n",
        "url = 'https://drive.google.com/uc?export=download&id=1fL7KPyxgGYfQDsuJoBWHIWwCAf-HTFpX'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Check data shape\n",
        "assert df.shape == (13958, 20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8BvDKLFy_Bq"
      },
      "source": [
        "To demonstrate mastery on your Sprint Challenge, do all the required, numbered instructions in this notebook.\n",
        "\n",
        "To earn a score of \"3\", also do all the stretch goals.\n",
        "\n",
        "You are permitted and encouraged to do as much data exploration as you want.\n",
        "\n",
        "**1. Begin with baselines for classification.** Your target to predict is `shot_made_flag`. What is your baseline accuracy, if you guessed the majority class for every prediction?\n",
        "\n",
        "**2. Hold out your test set.** Use the 2018-19 season to test. NBA seasons begin in October and end in June. You'll know you've split the data correctly when your test set has 1,709 observations.\n",
        "\n",
        "**3. Engineer new feature.** Engineer at least **1** new feature, from this list, or your own idea.\n",
        "- **Homecourt Advantage**: Is the home team (`htm`) the Golden State Warriors (`GSW`) ?\n",
        "- **Opponent**: Who is the other team playing the Golden State Warriors?\n",
        "- **Seconds remaining in the period**: Combine minutes remaining with seconds remaining, to get the total number of seconds remaining in the period.\n",
        "- **Seconds remaining in the game**: Combine period, and seconds remaining in the period, to get the total number of seconds remaining in the game. A basketball game has 4 periods, each 12 minutes long.\n",
        "- **Made previous shot**: Was Steph Curry's previous shot successful?\n",
        "\n",
        "**4. Decide how to validate** your model. Choose one of the following options. Any of these options are good. You are not graded on which you choose.\n",
        "- **Train/validate/test split: train on the 2009-10 season through 2016-17 season, validate with the 2017-18 season.** You'll know you've split the data correctly when your train set has 11,081 observations, and your validation set has 1,168 observations.\n",
        "- **Train/validate/test split: random 80/20%** train/validate split.\n",
        "- **Cross-validation** with independent test set. You may use any scikit-learn cross-validation method.\n",
        "\n",
        "**5.** Use a scikit-learn **pipeline** to **encode categoricals** and fit a **Decision Tree** or **Random Forest** model.\n",
        "\n",
        "**6.** Get your model's **validation accuracy.** (Multiple times if you try multiple iterations.) \n",
        "\n",
        "**7.** Get your model's **test accuracy.** (One time, at the end.)\n",
        "\n",
        "\n",
        "**8.** Given a **confusion matrix** for a hypothetical binary classification model, **calculate accuracy, precision, and recall.**\n",
        "\n",
        "### Stretch Goals\n",
        "- Engineer 4+ new features total, either from the list above, or your own ideas.\n",
        "- Make 2+ visualizations to explore relationships between features and target.\n",
        "- Optimize 3+ hyperparameters by trying 10+ \"candidates\" (possible combinations of hyperparameters). You can use `RandomizedSearchCV` or do it manually.\n",
        "- Get and plot your model's feature importances.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6Jt3qjQ-zig"
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      "source": [
        "## 1. Begin with baselines for classification. \n",
        "\n",
        ">Your target to predict is `shot_made_flag`. What would your baseline accuracy be, if you guessed the majority class for every prediction?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxG_Ogq7MKgD"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FdK63NiLh7n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "e7ffea27-e3f4-4c1f-e5b7-d1d9ddfac2f1"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>game_id</th>\n",
              "      <th>game_event_id</th>\n",
              "      <th>player_name</th>\n",
              "      <th>period</th>\n",
              "      <th>minutes_remaining</th>\n",
              "      <th>seconds_remaining</th>\n",
              "      <th>action_type</th>\n",
              "      <th>shot_type</th>\n",
              "      <th>shot_zone_basic</th>\n",
              "      <th>shot_zone_area</th>\n",
              "      <th>shot_zone_range</th>\n",
              "      <th>shot_distance</th>\n",
              "      <th>loc_x</th>\n",
              "      <th>loc_y</th>\n",
              "      <th>shot_made_flag</th>\n",
              "      <th>game_date</th>\n",
              "      <th>htm</th>\n",
              "      <th>vtm</th>\n",
              "      <th>season_type</th>\n",
              "      <th>scoremargin_before_shot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20900015</td>\n",
              "      <td>4</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>3PT Field Goal</td>\n",
              "      <td>Above the Break 3</td>\n",
              "      <td>Right Side Center(RC)</td>\n",
              "      <td>24+ ft.</td>\n",
              "      <td>26</td>\n",
              "      <td>99</td>\n",
              "      <td>249</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20900015</td>\n",
              "      <td>17</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>31</td>\n",
              "      <td>Step Back Jump shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side Center(LC)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>18</td>\n",
              "      <td>-122</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20900015</td>\n",
              "      <td>53</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>In The Paint (Non-RA)</td>\n",
              "      <td>Center(C)</td>\n",
              "      <td>8-16 ft.</td>\n",
              "      <td>14</td>\n",
              "      <td>-60</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>-4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20900015</td>\n",
              "      <td>141</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>49</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side(L)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>19</td>\n",
              "      <td>-172</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>-4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20900015</td>\n",
              "      <td>249</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side Center(LC)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>16</td>\n",
              "      <td>-68</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    game_id  game_event_id  ...     season_type  scoremargin_before_shot\n",
              "0  20900015              4  ...  Regular Season                      2.0\n",
              "1  20900015             17  ...  Regular Season                      0.0\n",
              "2  20900015             53  ...  Regular Season                     -4.0\n",
              "3  20900015            141  ...  Regular Season                     -4.0\n",
              "4  20900015            249  ...  Regular Season                      0.0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orayouyg4jgA"
      },
      "source": [
        "from datetime import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0BDeNFG_Kee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8989b02d-5ef7-4fb9-c0c4-b1d87b3641a3"
      },
      "source": [
        "guess = df['shot_made_flag'].mean()\n",
        "guess"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0.4729187562688064"
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          "metadata": {
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      "metadata": {
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          "height": 218
        },
        "outputId": "b2db37ca-0dbb-483d-8b34-e9e50b6e4418"
      },
      "source": [
        "df.game_date.astype('datetime64[M]')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2009-10-01\n",
              "1       2009-10-01\n",
              "2       2009-10-01\n",
              "3       2009-10-01\n",
              "4       2009-10-01\n",
              "           ...    \n",
              "13953   2019-06-01\n",
              "13954   2019-06-01\n",
              "13955   2019-06-01\n",
              "13956   2019-06-01\n",
              "13957   2019-06-01\n",
              "Name: game_date, Length: 13958, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
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      "cell_type": "code",
      "metadata": {
        "id": "DVKkSqXB5tKv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "9574284d-ff22-4ebf-e1a3-a1194fec7022"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "game_id                    0\n",
              "game_event_id              0\n",
              "player_name                0\n",
              "period                     0\n",
              "minutes_remaining          0\n",
              "seconds_remaining          0\n",
              "action_type                0\n",
              "shot_type                  0\n",
              "shot_zone_basic            0\n",
              "shot_zone_area             0\n",
              "shot_zone_range            0\n",
              "shot_distance              0\n",
              "loc_x                      0\n",
              "loc_y                      0\n",
              "shot_made_flag             0\n",
              "game_date                  0\n",
              "htm                        0\n",
              "vtm                        0\n",
              "season_type                0\n",
              "scoremargin_before_shot    0\n",
              "sec_rem_per                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dz2QHBiVy_Br"
      },
      "source": [
        "## 2. Hold out your test set.\n",
        "\n",
        ">Use the 2018-19 season to test. NBA seasons begin in October and end in June. You'll know you've split the data correctly when your test set has 1,709 observations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiIqOpdWxza9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e9ee526-8abf-4812-ae3b-d7f2ce213e94"
      },
      "source": [
        "train = df[df['game_date']<='2018-07-01']\n",
        "test = df[df['game_date']>='2018-09-01']\n",
        "\n",
        "\n",
        "test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1709, 21)"
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          "metadata": {
            "tags": []
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          "execution_count": 160
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      "metadata": {
        "id": "6bM6BSWn9r22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "254cde67-e4b8-4b63-be90-f21603d74199"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12249, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9Nihzk6y_CF"
      },
      "source": [
        "## 3. Engineer new feature.\n",
        "\n",
        ">Engineer at least **1** new feature, from this list, or your own idea.\n",
        ">\n",
        ">- **Homecourt Advantage**: Is the home team (`htm`) the Golden State Warriors (`GSW`) ?\n",
        ">- **Opponent**: Who is the other team playing the Golden State Warriors?\n",
        ">- **Seconds remaining in the period**: Combine minutes remaining with seconds remaining, to get the total number of seconds remaining in the period.\n",
        ">- **Seconds remaining in the game**: Combine period, and seconds remaining in the period, to get the total number of seconds remaining in the game. A basketball game has 4 periods, each 12 minutes long.\n",
        ">- **Made previous shot**: Was Steph Curry's previous shot successful?\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VukSrH8e-1EK"
      },
      "source": [
        "df['minutes_remaining'] = df['minutes_remaining']*60"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMdLdrFL--Ep",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "45c8dad5-ca74-4f13-f7af-61755062c416"
      },
      "source": [
        "df['minutes_remaining']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        39600\n",
              "1        32400\n",
              "2        21600\n",
              "3        32400\n",
              "4         7200\n",
              "         ...  \n",
              "13953    28800\n",
              "13954    25200\n",
              "13955    18000\n",
              "13956    14400\n",
              "13957     7200\n",
              "Name: minutes_remaining, Length: 13958, dtype: int64"
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          "execution_count": 63
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    {
      "cell_type": "code",
      "metadata": {
        "id": "A0pxdFtWy_Bz"
      },
      "source": [
        "df['sec_rem_per'] = df['minutes_remaining']+df['seconds_remaining']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "d915d743-5939-4a59-f7fb-0b90aa96abee"
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      "source": [
        "df.head()"
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              "      <th></th>\n",
              "      <th>game_id</th>\n",
              "      <th>game_event_id</th>\n",
              "      <th>player_name</th>\n",
              "      <th>period</th>\n",
              "      <th>minutes_remaining</th>\n",
              "      <th>seconds_remaining</th>\n",
              "      <th>action_type</th>\n",
              "      <th>shot_type</th>\n",
              "      <th>shot_zone_basic</th>\n",
              "      <th>shot_zone_area</th>\n",
              "      <th>shot_zone_range</th>\n",
              "      <th>shot_distance</th>\n",
              "      <th>loc_x</th>\n",
              "      <th>loc_y</th>\n",
              "      <th>shot_made_flag</th>\n",
              "      <th>game_date</th>\n",
              "      <th>htm</th>\n",
              "      <th>vtm</th>\n",
              "      <th>season_type</th>\n",
              "      <th>scoremargin_before_shot</th>\n",
              "      <th>sec_rem_per</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20900015</td>\n",
              "      <td>4</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>39600</td>\n",
              "      <td>25</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>3PT Field Goal</td>\n",
              "      <td>Above the Break 3</td>\n",
              "      <td>Right Side Center(RC)</td>\n",
              "      <td>24+ ft.</td>\n",
              "      <td>26</td>\n",
              "      <td>99</td>\n",
              "      <td>249</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20900015</td>\n",
              "      <td>17</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>32400</td>\n",
              "      <td>31</td>\n",
              "      <td>Step Back Jump shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side Center(LC)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>18</td>\n",
              "      <td>-122</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20900015</td>\n",
              "      <td>53</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>1</td>\n",
              "      <td>21600</td>\n",
              "      <td>2</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>In The Paint (Non-RA)</td>\n",
              "      <td>Center(C)</td>\n",
              "      <td>8-16 ft.</td>\n",
              "      <td>14</td>\n",
              "      <td>-60</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>21602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20900015</td>\n",
              "      <td>141</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>2</td>\n",
              "      <td>32400</td>\n",
              "      <td>49</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side(L)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>19</td>\n",
              "      <td>-172</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>32449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20900015</td>\n",
              "      <td>249</td>\n",
              "      <td>Stephen Curry</td>\n",
              "      <td>2</td>\n",
              "      <td>7200</td>\n",
              "      <td>19</td>\n",
              "      <td>Jump Shot</td>\n",
              "      <td>2PT Field Goal</td>\n",
              "      <td>Mid-Range</td>\n",
              "      <td>Left Side Center(LC)</td>\n",
              "      <td>16-24 ft.</td>\n",
              "      <td>16</td>\n",
              "      <td>-68</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>2009-10-28</td>\n",
              "      <td>GSW</td>\n",
              "      <td>HOU</td>\n",
              "      <td>Regular Season</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    game_id  game_event_id  ... scoremargin_before_shot  sec_rem_per\n",
              "0  20900015              4  ...                     2.0        39625\n",
              "1  20900015             17  ...                     0.0        32431\n",
              "2  20900015             53  ...                    -4.0        21602\n",
              "3  20900015            141  ...                    -4.0        32449\n",
              "4  20900015            249  ...                     0.0         7219\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLs7pt7NFJLF"
      },
      "source": [
        "## **4. Decide how to validate** your model. \n",
        "\n",
        ">Choose one of the following options. Any of these options are good. You are not graded on which you choose.\n",
        ">\n",
        ">- **Train/validate/test split: train on the 2009-10 season through 2016-17 season, validate with the 2017-18 season.** You'll know you've split the data correctly when your train set has 11,081 observations, and your validation set has 1,168 observations.\n",
        ">- **Train/validate/test split: random 80/20%** train/validate split.\n",
        ">- **Cross-validation** with independent test set. You may use any scikit-learn cross-validation method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MyiQ0CsBSY5"
      },
      "source": [
        "trained = train[train['game_date']<='2017-07-01']\n",
        "val = train[train['game_date']>='2017-09-01']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ58CceDISXR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd7a9214-5318-40ca-b49e-c720acd77250"
      },
      "source": [
        "trained.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11081, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E9kEwQvCSzQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c162b06-4121-48f3-a36b-9ae12578d608"
      },
      "source": [
        "val.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1168, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pL_uNX8xFWHz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "627d6f74-ae77-4fde-be93-0337a9620b95"
      },
      "source": [
        "trained['game_date'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2013-05-06    0.003159\n",
              "2016-05-09    0.002888\n",
              "2013-12-09    0.002888\n",
              "2013-04-12    0.002798\n",
              "2015-11-14    0.002798\n",
              "                ...   \n",
              "2009-11-30    0.000451\n",
              "2011-02-15    0.000451\n",
              "2009-12-28    0.000361\n",
              "2012-03-11    0.000271\n",
              "2010-12-08    0.000180\n",
              "Name: game_date, Length: 647, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoniCiRjGZ2D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "79aecb28-ac51-43bb-c215-8105216293c8"
      },
      "source": [
        "trained.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "game_id                             int64\n",
              "game_event_id                       int64\n",
              "player_name                        object\n",
              "period                              int64\n",
              "minutes_remaining                   int64\n",
              "seconds_remaining                   int64\n",
              "action_type                        object\n",
              "shot_type                          object\n",
              "shot_zone_basic                    object\n",
              "shot_zone_area                     object\n",
              "shot_zone_range                    object\n",
              "shot_distance                       int64\n",
              "loc_x                               int64\n",
              "loc_y                               int64\n",
              "shot_made_flag                      int64\n",
              "game_date                  datetime64[ns]\n",
              "htm                                object\n",
              "vtm                                object\n",
              "season_type                        object\n",
              "scoremargin_before_shot           float64\n",
              "sec_rem_per                         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcdqo-rvIoX8"
      },
      "source": [
        "fin = trained.drop(['shot_made_flag'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQ2lWlu7JPRt"
      },
      "source": [
        "## 5. Use a scikit-learn pipeline to encode categoricals and fit a Decision Tree or Random Forest model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2S8mUuJy_CB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ea5dcef4-17a7-4e9d-c936-84253cb990c2"
      },
      "source": [
        "numeric_features = fin.select_dtypes(include='number').columns.tolist()\n",
        "\n",
        "cardinality = fin.select_dtypes(exclude='number').nunique()\n",
        "\n",
        "categorical_features = cardinality[cardinality <= 100].index.tolist()\n",
        "\n",
        "\n",
        "features = numeric_features + categorical_features\n",
        "print(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['game_id', 'game_event_id', 'period', 'minutes_remaining', 'seconds_remaining', 'shot_distance', 'loc_x', 'loc_y', 'scoremargin_before_shot', 'sec_rem_per', 'player_name', 'action_type', 'shot_type', 'shot_zone_basic', 'shot_zone_area', 'shot_zone_range', 'htm', 'vtm', 'season_type']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLx3B0DyDWqA"
      },
      "source": [
        "target = trained['shot_made_flag']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3Vcuo0dDWtn"
      },
      "source": [
        "X_train = fin[features]\n",
        "y_train = trained['shot_made_flag']\n",
        "X_val = val[features]\n",
        "y_val = val['shot_made_flag']\n",
        "X_test = test[features]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kJXxFpty_CH"
      },
      "source": [
        "## 6.Get your model's validation accuracy\n",
        "\n",
        "> (Multiple times if you try multiple iterations.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsJjEnjiEZ1p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1d1c4ad2-1524-44c6-b8c7-e2c5d5d7b72a"
      },
      "source": [
        "import category_encoders as ce\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "pipeline = make_pipeline(\n",
        "    ce.OrdinalEncoder(),\n",
        "    SimpleImputer(strategy='mean'), \n",
        "    StandardScaler(), \n",
        "    RandomForestClassifier(n_estimators=400, \n",
        "                               bootstrap = True,\n",
        "                               max_features = 'sqrt')\n",
        ")\n",
        "\n",
        "\n",
        "# Fit on train\n",
        "pipeline.fit(X_train, y_train)\n",
        "\n",
        "# Score on val\n",
        "print('Validation Accuracy', pipeline.score(X_val, y_val))\n",
        "\n",
        "# Predict on test\n",
        "y_pred = pipeline.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy 0.583904109589041\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-TsR5duiyz3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b384533-fca2-4491-be8b-302555f13c02"
      },
      "source": [
        "y_pred.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1709,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvyYY9tfy_CL"
      },
      "source": [
        "## 7. Get your model's test accuracy\n",
        "\n",
        "> (One time, at the end.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQq4eS6Ai7nT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97c5cfa3-48dd-4157-bc74-30f597f15d65"
      },
      "source": [
        "test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1709, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VX3eTYZ_i-Xx"
      },
      "source": [
        "y_test = test['shot_made_flag']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYzTciD3hAvL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c3ce59d5-4872-45fc-bade-291057860c98"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6179052077238151"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGL5stLvJCn1"
      },
      "source": [
        "## 8. Given a confusion matrix, calculate accuracy, precision, and recall.\n",
        "\n",
        "Imagine this is the confusion matrix for a binary classification model. Use the confusion matrix to calculate the model's accuracy, precision, and recall.\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <td colspan=\"2\" rowspan=\"2\"></td>\n",
        "    <td colspan=\"2\">Predicted</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Negative</td>\n",
        "    <td>Positive</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td rowspan=\"2\">Actual</td>\n",
        "    <td>Negative</td>\n",
        "    <td style=\"border: solid\">85</td>\n",
        "    <td style=\"border: solid\">58</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Positive</td>\n",
        "    <td style=\"border: solid\">8</td>\n",
        "    <td style=\"border: solid\"> 36</td>\n",
        "  </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEvt7NkUJNao"
      },
      "source": [
        "### Calculate accuracy "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFszS2A5JJmv"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtJpA84KkQuY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f77d3b74-0cd9-45f3-c15d-15661fc7f0fa"
      },
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[546, 366],\n",
              "       [287, 510]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjHTmk8sJO4v"
      },
      "source": [
        "### Calculate precision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFug3ZKaJQ7A"
      },
      "source": [
        "### Calculate recall"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qX1gbcMJQS_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4066a60a-a784-43dc-9262-4c7b0a3fc3a9"
      },
      "source": [
        "metrx = {'precision': metrics.precision_score(y_test, y_pred),\n",
        "         'recall': metrics.recall_score(y_test, y_pred),\n",
        "         'f1': metrics.f1_score(y_test, y_pred)}\n",
        "print(metrx)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'precision': 0.5821917808219178, 'recall': 0.6398996235884568, 'f1': 0.6096832038254633}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0OKc3JxJR4r"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}