PySpark_Python_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PySpark_Python_Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNnLD/6bLOhI3KWdNzSUmcs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Bang2018/SPARK-ML/blob/main/PySpark_Python_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAN1p0PI6geA"
      },
      "source": [
        "**Installation:**\n",
        "\n",
        "1.Java 8\n",
        "\n",
        "2.Apache Spark 3.0.0 with hadoop 3.2\n",
        "\n",
        "3.Findspark to locate the Spark in the System\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh4qxZjH6Ib2"
      },
      "source": [
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcsmmF797fQy"
      },
      "source": [
        "# install spark (change the version number if needed)\n",
        "!wget -q https://archive.apache.org/dist/spark/spark-3.0.0/spark-3.0.0-bin-hadoop3.2.tgz\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFLgpGk77j4V"
      },
      "source": [
        "# unzip it\n",
        "!tar xf spark-3.0.0-bin-hadoop3.2.tgz\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPwXGkzeMTi1"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZokcFNjkD995"
      },
      "source": [
        "!pip install -q findspark"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXhoeMCDEL9x"
      },
      "source": [
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.0.0-bin-hadoop3.2\""
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpzE71aoEiBh"
      },
      "source": [
        "import findspark\n",
        "findspark.init()\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "-hyxFriHEwB_",
        "outputId": "8409d559-af23-4a59-dbdb-1a0560920437"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fe7b5d03-a200-4ba9-8af6-92c6262d3c8c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fe7b5d03-a200-4ba9-8af6-92c6262d3c8c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving BostonHousing.csv to BostonHousing.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'BostonHousing.csv': 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20.2,368.74,18.13,19.1\\n13.0751,0,18.1,\"0\",0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1\\n4.34879,0,18.1,\"0\",0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9\\n4.03841,0,18.1,\"0\",0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6\\n3.56868,0,18.1,\"0\",0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2\\n4.64689,0,18.1,\"0\",0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8\\n8.05579,0,18.1,\"0\",0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8\\n6.39312,0,18.1,\"0\",0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3\\n4.87141,0,18.1,\"0\",0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7\\n15.0234,0,18.1,\"0\",0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12\\n10.233,0,18.1,\"0\",0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6\\n14.3337,0,18.1,\"0\",0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4\\n5.82401,0,18.1,\"0\",0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23\\n5.70818,0,18.1,\"0\",0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7\\n5.73116,0,18.1,\"0\",0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25\\n2.81838,0,18.1,\"0\",0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8\\n2.37857,0,18.1,\"0\",0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6\\n3.67367,0,18.1,\"0\",0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2\\n5.69175,0,18.1,\"0\",0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1\\n4.83567,0,18.1,\"0\",0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6\\n0.15086,0,27.74,\"0\",0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2\\n0.18337,0,27.74,\"0\",0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7\\n0.20746,0,27.74,\"0\",0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1\\n0.10574,0,27.74,\"0\",0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6\\n0.11132,0,27.74,\"0\",0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1\\n0.17331,0,9.69,\"0\",0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8\\n0.27957,0,9.69,\"0\",0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5\\n0.17899,0,9.69,\"0\",0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1\\n0.2896,0,9.69,\"0\",0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7\\n0.26838,0,9.69,\"0\",0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3\\n0.23912,0,9.69,\"0\",0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2\\n0.17783,0,9.69,\"0\",0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5\\n0.22438,0,9.69,\"0\",0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8\\n0.06263,0,11.93,\"0\",0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4\\n0.04527,0,11.93,\"0\",0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6\\n0.06076,0,11.93,\"0\",0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9\\n0.10959,0,11.93,\"0\",0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22\\n0.04741,0,11.93,\"0\",0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9\\n'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2RElth1GAa_",
        "outputId": "e568c18e-b758-46a1-b1e0-a0df7af77ce2"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BostonHousing.csv  spark-3.0.0-bin-hadoop3.2\n",
            "sample_data\t   spark-3.0.0-bin-hadoop3.2.tgz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EE0Ws9IpGFoc"
      },
      "source": [
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml.regression import LinearRegression\n",
        "\n",
        "dataset = spark.read.csv('BostonHousing.csv',inferSchema=True, header =True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6tScuL8kjv8f",
        "outputId": "35a61783-876a-469f-a65e-29ae25ddb1d9"
      },
      "source": [
        "dataset.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------+----+-----+----+-----+-----+-----+------+---+---+-------+------+-----+----+\n",
            "|   crim|  zn|indus|chas|  nox|   rm|  age|   dis|rad|tax|ptratio|     b|lstat|medv|\n",
            "+-------+----+-----+----+-----+-----+-----+------+---+---+-------+------+-----+----+\n",
            "|0.00632|18.0| 2.31|   0|0.538|6.575| 65.2|  4.09|  1|296|   15.3| 396.9| 4.98|24.0|\n",
            "|0.02731| 0.0| 7.07|   0|0.469|6.421| 78.9|4.9671|  2|242|   17.8| 396.9| 9.14|21.6|\n",
            "|0.02729| 0.0| 7.07|   0|0.469|7.185| 61.1|4.9671|  2|242|   17.8|392.83| 4.03|34.7|\n",
            "|0.03237| 0.0| 2.18|   0|0.458|6.998| 45.8|6.0622|  3|222|   18.7|394.63| 2.94|33.4|\n",
            "|0.06905| 0.0| 2.18|   0|0.458|7.147| 54.2|6.0622|  3|222|   18.7| 396.9| 5.33|36.2|\n",
            "|0.02985| 0.0| 2.18|   0|0.458| 6.43| 58.7|6.0622|  3|222|   18.7|394.12| 5.21|28.7|\n",
            "|0.08829|12.5| 7.87|   0|0.524|6.012| 66.6|5.5605|  5|311|   15.2| 395.6|12.43|22.9|\n",
            "|0.14455|12.5| 7.87|   0|0.524|6.172| 96.1|5.9505|  5|311|   15.2| 396.9|19.15|27.1|\n",
            "|0.21124|12.5| 7.87|   0|0.524|5.631|100.0|6.0821|  5|311|   15.2|386.63|29.93|16.5|\n",
            "|0.17004|12.5| 7.87|   0|0.524|6.004| 85.9|6.5921|  5|311|   15.2|386.71| 17.1|18.9|\n",
            "|0.22489|12.5| 7.87|   0|0.524|6.377| 94.3|6.3467|  5|311|   15.2|392.52|20.45|15.0|\n",
            "|0.11747|12.5| 7.87|   0|0.524|6.009| 82.9|6.2267|  5|311|   15.2| 396.9|13.27|18.9|\n",
            "|0.09378|12.5| 7.87|   0|0.524|5.889| 39.0|5.4509|  5|311|   15.2| 390.5|15.71|21.7|\n",
            "|0.62976| 0.0| 8.14|   0|0.538|5.949| 61.8|4.7075|  4|307|   21.0| 396.9| 8.26|20.4|\n",
            "|0.63796| 0.0| 8.14|   0|0.538|6.096| 84.5|4.4619|  4|307|   21.0|380.02|10.26|18.2|\n",
            "|0.62739| 0.0| 8.14|   0|0.538|5.834| 56.5|4.4986|  4|307|   21.0|395.62| 8.47|19.9|\n",
            "|1.05393| 0.0| 8.14|   0|0.538|5.935| 29.3|4.4986|  4|307|   21.0|386.85| 6.58|23.1|\n",
            "| 0.7842| 0.0| 8.14|   0|0.538| 5.99| 81.7|4.2579|  4|307|   21.0|386.75|14.67|17.5|\n",
            "|0.80271| 0.0| 8.14|   0|0.538|5.456| 36.6|3.7965|  4|307|   21.0|288.99|11.69|20.2|\n",
            "| 0.7258| 0.0| 8.14|   0|0.538|5.727| 69.5|3.7965|  4|307|   21.0|390.95|11.28|18.2|\n",
            "+-------+----+-----+----+-----+-----+-----+------+---+---+-------+------+-----+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d34Pzc5y1WdU",
        "outputId": "77eb3267-c9d7-4039-e772-badc46bc966b"
      },
      "source": [
        "spark.catalog.listTables()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uflZzzioGQpB",
        "outputId": "8f8edc2a-4de1-48cc-c332-7ec8c3e78f57"
      },
      "source": [
        "dataset.printSchema()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- crim: double (nullable = true)\n",
            " |-- zn: double (nullable = true)\n",
            " |-- indus: double (nullable = true)\n",
            " |-- chas: integer (nullable = true)\n",
            " |-- nox: double (nullable = true)\n",
            " |-- rm: double (nullable = true)\n",
            " |-- age: double (nullable = true)\n",
            " |-- dis: double (nullable = true)\n",
            " |-- rad: integer (nullable = true)\n",
            " |-- tax: integer (nullable = true)\n",
            " |-- ptratio: double (nullable = true)\n",
            " |-- b: double (nullable = true)\n",
            " |-- lstat: double (nullable = true)\n",
            " |-- medv: double (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_41T-vuGZVO",
        "outputId": "f0e28d7e-d0e6-42fb-edd0-d747a27b9ede"
      },
      "source": [
        "#Input all the features in one vector column\n",
        "assembler = VectorAssembler(inputCols=['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'b', 'lstat'], outputCol = 'Attributes')\n",
        "\n",
        "output = assembler.transform(dataset)\n",
        "\n",
        "#Input vs Output\n",
        "finalized_data = output.select(\"Attributes\",\"medv\")\n",
        "\n",
        "finalized_data.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+----+\n",
            "|          Attributes|medv|\n",
            "+--------------------+----+\n",
            "|[0.00632,18.0,2.3...|24.0|\n",
            "|[0.02731,0.0,7.07...|21.6|\n",
            "|[0.02729,0.0,7.07...|34.7|\n",
            "|[0.03237,0.0,2.18...|33.4|\n",
            "|[0.06905,0.0,2.18...|36.2|\n",
            "|[0.02985,0.0,2.18...|28.7|\n",
            "|[0.08829,12.5,7.8...|22.9|\n",
            "|[0.14455,12.5,7.8...|27.1|\n",
            "|[0.21124,12.5,7.8...|16.5|\n",
            "|[0.17004,12.5,7.8...|18.9|\n",
            "|[0.22489,12.5,7.8...|15.0|\n",
            "|[0.11747,12.5,7.8...|18.9|\n",
            "|[0.09378,12.5,7.8...|21.7|\n",
            "|[0.62976,0.0,8.14...|20.4|\n",
            "|[0.63796,0.0,8.14...|18.2|\n",
            "|[0.62739,0.0,8.14...|19.9|\n",
            "|[1.05393,0.0,8.14...|23.1|\n",
            "|[0.7842,0.0,8.14,...|17.5|\n",
            "|[0.80271,0.0,8.14...|20.2|\n",
            "|[0.7258,0.0,8.14,...|18.2|\n",
            "+--------------------+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1vaTD1UGn0v",
        "outputId": "b2318730-a54d-4224-b3f5-400a430ac3f9"
      },
      "source": [
        "#Split training and testing data\n",
        "train_data,test_data = finalized_data.randomSplit([0.8,0.2])\n",
        "\n",
        "\n",
        "regressor = LinearRegression(featuresCol = 'Attributes', labelCol = 'medv')\n",
        "\n",
        "#Learn to fit the model from training set\n",
        "regressor = regressor.fit(train_data)\n",
        "\n",
        "#To predict the prices on testing set\n",
        "pred = regressor.evaluate(test_data)\n",
        "\n",
        "#Predict the model\n",
        "pred.predictions.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+----+------------------+\n",
            "|          Attributes|medv|        prediction|\n",
            "+--------------------+----+------------------+\n",
            "|[0.01311,90.0,1.2...|35.4|30.426217077691824|\n",
            "|[0.01709,90.0,2.0...|30.1| 24.32553524666185|\n",
            "|[0.03049,55.0,3.7...|31.2| 28.42895388792563|\n",
            "|[0.03427,0.0,5.19...|19.5| 19.86419878109184|\n",
            "|[0.03466,35.0,6.0...|19.4|23.623093121259362|\n",
            "|[0.0351,95.0,2.68...|48.5| 41.92099484715463|\n",
            "|[0.03578,20.0,3.3...|45.4|38.191228289453775|\n",
            "|[0.03584,80.0,3.3...|23.5|30.942461810937786|\n",
            "|[0.03871,52.5,5.3...|23.2|27.117375597223322|\n",
            "|[0.03961,0.0,5.19...|21.1|20.162865557505164|\n",
            "|[0.04337,21.0,5.6...|20.5|  23.7761882437657|\n",
            "|[0.04544,0.0,3.24...|19.8| 21.83757157636007|\n",
            "|[0.0456,0.0,13.89...|23.3| 26.39239094898357|\n",
            "|[0.04819,80.0,3.6...|21.9|24.110840536675763|\n",
            "|[0.05023,35.0,6.0...|17.1|20.366986027657955|\n",
            "|[0.05059,0.0,4.49...|23.9|24.590463903385306|\n",
            "|[0.0578,0.0,2.46,...|37.2| 32.72172798547749|\n",
            "|[0.06162,0.0,4.39...|17.2|14.362454036597551|\n",
            "|[0.06466,70.0,2.2...|22.5| 29.49966336405612|\n",
            "|[0.06664,0.0,4.05...|29.4|31.024141368162095|\n",
            "+--------------------+----+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAPWtIebGxVm",
        "outputId": "7e75d3a5-035a-4ce7-d156-7ced5575ee2f"
      },
      "source": [
        "#coefficient of the regression model\n",
        "coeff = regressor.coefficients\n",
        "\n",
        "#X and Y intercept\n",
        "intr = regressor.intercept\n",
        "\n",
        "print (\"The coefficient of the model is : %a\" %coeff)\n",
        "print (\"The Intercept of the model is : %f\" %intr)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The coefficient of the model is : DenseVector([-0.1241, 0.0516, 0.0191, 2.097, -18.8757, 3.2182, -0.0028, -1.6568, 0.2814, -0.0106, -1.0513, 0.0098, -0.5447])\n",
            "The Intercept of the model is : 43.048189\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNmls1vTG3lH",
        "outputId": "10fd8236-23c6-4d83-9df6-0649e1d26e46"
      },
      "source": [
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "eval = RegressionEvaluator(labelCol=\"medv\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
        "\n",
        "# Root Mean Square Error\n",
        "rmse = eval.evaluate(pred.predictions)\n",
        "print(\"RMSE: %.3f\" % rmse)\n",
        "\n",
        "# Mean Square Error\n",
        "mse = eval.evaluate(pred.predictions, {eval.metricName: \"mse\"})\n",
        "print(\"MSE: %.3f\" % mse)\n",
        "\n",
        "# Mean Absolute Error\n",
        "mae = eval.evaluate(pred.predictions, {eval.metricName: \"mae\"})\n",
        "print(\"MAE: %.3f\" % mae)\n",
        "\n",
        "# r2 - coefficient of determination\n",
        "r2 = eval.evaluate(pred.predictions, {eval.metricName: \"r2\"})\n",
        "print(\"r2: %.3f\" %r2)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 4.761\n",
            "MSE: 22.670\n",
            "MAE: 3.084\n",
            "r2: 0.730\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}