Spark_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " Spark_Final_Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02IYg6_F6yoY",
        "outputId": "b4aae6fd-bb7c-4816-990e-b7c429ede658"
      },
      "source": [
        "!pip install pyspark"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyspark in /usr/local/lib/python3.7/dist-packages (3.1.2)\n",
            "Requirement already satisfied: py4j==0.10.9 in /usr/local/lib/python3.7/dist-packages (from pyspark) (0.10.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HllrTE826-Iz"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import pyspark"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3t7AY4lLNSNA"
      },
      "source": [
        "from pyspark.sql import SparkSession"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfBQuZyYNdO6"
      },
      "source": [
        "spark = SparkSession.builder.appName(\"CarPrediction\").getOrCreate()"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsaRSaVtNnLJ"
      },
      "source": [
        "# Using read.csv to read the dataset\n",
        "df = spark.read.csv(\"./audi.csv\", inferSchema=True, header=True)"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ItI28MfiSmv",
        "outputId": "121b29e1-9e46-437e-99ca-2fef8e2b4c61"
      },
      "source": [
        "# Checks if there are any null values in the column\n",
        "for col in df.columns:\n",
        "  print(col, \"\\t\", \"with null values: \", df.filter(df[col].isNull()).count())"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model \t with null values:  0\n",
            "year \t with null values:  0\n",
            "price \t with null values:  0\n",
            "transmission \t with null values:  0\n",
            "mileage \t with null values:  0\n",
            "fuelType \t with null values:  0\n",
            "tax \t with null values:  0\n",
            "mpg \t with null values:  0\n",
            "engineSize \t with null values:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaAqG-Dnir89",
        "outputId": "16bfdd04-f46e-4661-9382-04e1e8c0c2a5"
      },
      "source": [
        "# Checks if there are any values with '?'\n",
        "for col in df.columns:\n",
        "  print(col, \"\\t\", \"with ? values: \", df.filter(df[col]==\"?\").count())"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model \t with ? values:  0\n",
            "year \t with ? values:  0\n",
            "price \t with ? values:  0\n",
            "transmission \t with ? values:  0\n",
            "mileage \t with ? values:  0\n",
            "fuelType \t with ? values:  0\n",
            "tax \t with ? values:  0\n",
            "mpg \t with ? values:  0\n",
            "engineSize \t with ? values:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8tKID0PNwJ6",
        "outputId": "ccf95165-5cce-44b5-a394-bdd43e8f7734"
      },
      "source": [
        "df.printSchema() # outputs the schema types which are automatically determined by spark"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root\n",
            " |-- model: string (nullable = true)\n",
            " |-- year: integer (nullable = true)\n",
            " |-- price: integer (nullable = true)\n",
            " |-- transmission: string (nullable = true)\n",
            " |-- mileage: integer (nullable = true)\n",
            " |-- fuelType: string (nullable = true)\n",
            " |-- tax: integer (nullable = true)\n",
            " |-- mpg: double (nullable = true)\n",
            " |-- engineSize: double (nullable = true)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UpekeFCP0wp",
        "outputId": "5b2cb670-e84b-49ae-d1b0-bf0311684ba0"
      },
      "source": [
        "df.show()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "|model|year|price|transmission|mileage|fuelType|tax| mpg|engineSize|\n",
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "|   A1|2017|12500|      Manual|  15735|  Petrol|150|55.4|       1.4|\n",
            "|   A6|2016|16500|   Automatic|  36203|  Diesel| 20|64.2|       2.0|\n",
            "|   A1|2016|11000|      Manual|  29946|  Petrol| 30|55.4|       1.4|\n",
            "|   A4|2017|16800|   Automatic|  25952|  Diesel|145|67.3|       2.0|\n",
            "|   A3|2019|17300|      Manual|   1998|  Petrol|145|49.6|       1.0|\n",
            "|   A1|2016|13900|   Automatic|  32260|  Petrol| 30|58.9|       1.4|\n",
            "|   A6|2016|13250|   Automatic|  76788|  Diesel| 30|61.4|       2.0|\n",
            "|   A4|2016|11750|      Manual|  75185|  Diesel| 20|70.6|       2.0|\n",
            "|   A3|2015|10200|      Manual|  46112|  Petrol| 20|60.1|       1.4|\n",
            "|   A1|2016|12000|      Manual|  22451|  Petrol| 30|55.4|       1.4|\n",
            "|   A3|2017|16100|      Manual|  28955|  Petrol|145|58.9|       1.4|\n",
            "|   A6|2016|16500|   Automatic|  52198|  Diesel|125|57.6|       2.0|\n",
            "|   Q3|2016|17000|      Manual|  44915|  Diesel|145|52.3|       2.0|\n",
            "|   A3|2017|16400|      Manual|  21695|  Petrol| 30|58.9|       1.4|\n",
            "|   A6|2015|15400|      Manual|  47348|  Diesel| 30|61.4|       2.0|\n",
            "|   A3|2017|14500|   Automatic|  26156|  Petrol|145|58.9|       1.4|\n",
            "|   Q3|2016|15700|   Automatic|  28396|  Diesel|145|53.3|       2.0|\n",
            "|   A3|2014|13900|   Automatic|  30516|  Petrol| 30|56.5|       1.4|\n",
            "|   Q5|2016|19000|   Automatic|  37652|  Diesel|200|47.1|       2.0|\n",
            "|   Q3|2014|17000|      Manual|  34110|  Petrol|145|47.9|       1.4|\n",
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2O3pk7YZP-Jw",
        "outputId": "46bb84a3-fdc0-4e2c-9caf-6a8eb61f3cb3"
      },
      "source": [
        "# Checking the columns of the dataframe \n",
        "df.columns"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model',\n",
              " 'year',\n",
              " 'price',\n",
              " 'transmission',\n",
              " 'mileage',\n",
              " 'fuelType',\n",
              " 'tax',\n",
              " 'mpg',\n",
              " 'engineSize']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pu9Yb1frQBDv",
        "outputId": "9607548f-19cd-423d-a4cf-6f931766ee64"
      },
      "source": [
        "# Checking the type of each column in the dataframe\n",
        "df.describe()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[summary: string, model: string, year: string, price: string, transmission: string, mileage: string, fuelType: string, tax: string, mpg: string, engineSize: string]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ghz_hBaaN-lY",
        "outputId": "39fb7bec-4d5e-451a-87fb-b9697337c11b"
      },
      "source": [
        "# taking the cars whose price is less than 15000 and the transmission is Manual\n",
        "df.filter((df[\"transmission\"] == \"Manual\") & (df[\"price\"]<15000)).show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "|model|year|price|transmission|mileage|fuelType|tax| mpg|engineSize|\n",
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "|   A1|2017|12500|      Manual|  15735|  Petrol|150|55.4|       1.4|\n",
            "|   A1|2016|11000|      Manual|  29946|  Petrol| 30|55.4|       1.4|\n",
            "|   A4|2016|11750|      Manual|  75185|  Diesel| 20|70.6|       2.0|\n",
            "|   A3|2015|10200|      Manual|  46112|  Petrol| 20|60.1|       1.4|\n",
            "|   A1|2016|12000|      Manual|  22451|  Petrol| 30|55.4|       1.4|\n",
            "|   A6|2015|14000|      Manual|  61176|  Diesel| 30|62.8|       2.0|\n",
            "|   A1|2017|10800|      Manual|  31447|  Petrol|145|67.3|       1.0|\n",
            "|   A3|2015|11300|      Manual|  75619|  Diesel| 20|68.9|       2.0|\n",
            "|   A4|2016|12750|      Manual|  52561|  Diesel|  0|74.3|       2.0|\n",
            "|   A1|2014|11000|      Manual|  34315|  Petrol|125|53.3|       1.4|\n",
            "|   A3|2016|10200|      Manual|  66369|  Diesel|  0|83.1|       1.6|\n",
            "|   A1|2013| 7000|      Manual|  76269|  Diesel|  0|74.3|       1.6|\n",
            "|   A3|2016|10600|      Manual|  37840|  Diesel| 20|70.6|       1.6|\n",
            "|   Q3|2017|14000|      Manual|  67161|  Petrol|150|51.4|       1.4|\n",
            "|   A4|2016|10200|      Manual|  97440|  Diesel| 20|70.6|       2.0|\n",
            "|   A1|2017|11700|      Manual|  19268|  Petrol|150|67.3|       1.0|\n",
            "|   A1|2016|10800|      Manual|  18181|  Petrol|  0|67.3|       1.0|\n",
            "|   A3|2016|12500|      Manual|  19494|  Diesel| 30|67.3|       2.0|\n",
            "|   A3|2017|13100|      Manual|  25965|  Diesel|145|65.7|       2.0|\n",
            "|   A3|2016| 8300|      Manual|  88215|  Diesel| 20|68.9|       2.0|\n",
            "+-----+----+-----+------------+-------+--------+---+----+----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ij3TlG5LOkh6",
        "outputId": "5c94ccd9-c3a1-44e1-bed4-b47dd0087f9a"
      },
      "source": [
        "df.groupBy(\"model\")"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyspark.sql.group.GroupedData at 0x7f9a98282ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdj9ipZ6Qhp4",
        "outputId": "0c17c58c-dbb4-45be-a86a-343480adbd79"
      },
      "source": [
        "df.groupBy(\"model\").count().show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+-----+\n",
            "|model|count|\n",
            "+-----+-----+\n",
            "|   A7|  122|\n",
            "|  SQ7|    8|\n",
            "|   A4| 1381|\n",
            "|  RS3|   33|\n",
            "|   S3|   18|\n",
            "|  RS5|   29|\n",
            "|  RS6|   39|\n",
            "|  SQ5|   16|\n",
            "|   S4|   12|\n",
            "|   Q8|   69|\n",
            "|  RS4|   31|\n",
            "|   A1| 1347|\n",
            "|   A3| 1929|\n",
            "|   Q2|  822|\n",
            "|  RS7|    1|\n",
            "|   Q7|  397|\n",
            "|   Q3| 1417|\n",
            "|   R8|   28|\n",
            "|   S5|    3|\n",
            "|   Q5|  877|\n",
            "+-----+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xT4RWG7vQmJ8",
        "outputId": "8a62ec04-0919-44c7-8216-6588840d696f"
      },
      "source": [
        "df.groupby(\"model\").mean()[[\"model\",\"avg(price)\"]].show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+------------------+\n",
            "|model|        avg(price)|\n",
            "+-----+------------------+\n",
            "|   A7|28865.516393442624|\n",
            "|  SQ7|           49269.0|\n",
            "|   A4|20255.450398262128|\n",
            "|  RS3| 34050.51515151515|\n",
            "|   S3|20379.444444444445|\n",
            "|  RS5|51265.206896551725|\n",
            "|  RS6|  55963.8717948718|\n",
            "|  SQ5|        31415.8125|\n",
            "|   S4|31248.083333333332|\n",
            "|   Q8|60115.014492753624|\n",
            "|  RS4|  50151.6129032258|\n",
            "|   A1|14327.750556792873|\n",
            "|   A3|17408.522032141005|\n",
            "|   Q2|22516.975669099756|\n",
            "|  RS7|           33490.0|\n",
            "|   Q7|44788.319899244336|\n",
            "|   Q3| 22999.26182074806|\n",
            "|   R8| 97652.21428571429|\n",
            "|   S5|           15980.0|\n",
            "|   Q5|30445.688711516534|\n",
            "+-----+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAzzu4xydTJB",
        "outputId": "ca65fd7e-d792-4f11-e73f-1cee4e7c288e"
      },
      "source": [
        "df.groupBy(\"year\").mean()[[\"year\",\"avg(tax)\"]].show()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+----+------------------+\n",
            "|year|          avg(tax)|\n",
            "+----+------------------+\n",
            "|2003|255.83333333333334|\n",
            "|2007|          340.9375|\n",
            "|2018| 146.1747685185185|\n",
            "|2015| 98.95395395395396|\n",
            "|2006| 371.1111111111111|\n",
            "|2013|125.97938144329896|\n",
            "|1997|             265.0|\n",
            "|2014| 129.0268456375839|\n",
            "|2019|  145.573975602127|\n",
            "|2004|             319.0|\n",
            "|1998|             265.0|\n",
            "|2020| 145.5377094972067|\n",
            "|2012|132.11764705882354|\n",
            "|2009|223.47826086956522|\n",
            "|2016| 85.06364562118127|\n",
            "|2005| 269.2857142857143|\n",
            "|2010|            176.25|\n",
            "|2011| 160.2127659574468|\n",
            "|2008|252.38095238095238|\n",
            "|2017|123.17054263565892|\n",
            "+----+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJMQ1IeXdzex",
        "outputId": "b6476f66-0cb6-4d9e-b453-1672d64df1c1"
      },
      "source": [
        "df.take(10)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(model=' A1', year=2017, price=12500, transmission='Manual', mileage=15735, fuelType='Petrol', tax=150, mpg=55.4, engineSize=1.4),\n",
              " Row(model=' A6', year=2016, price=16500, transmission='Automatic', mileage=36203, fuelType='Diesel', tax=20, mpg=64.2, engineSize=2.0),\n",
              " Row(model=' A1', year=2016, price=11000, transmission='Manual', mileage=29946, fuelType='Petrol', tax=30, mpg=55.4, engineSize=1.4),\n",
              " Row(model=' A4', year=2017, price=16800, transmission='Automatic', mileage=25952, fuelType='Diesel', tax=145, mpg=67.3, engineSize=2.0),\n",
              " Row(model=' A3', year=2019, price=17300, transmission='Manual', mileage=1998, fuelType='Petrol', tax=145, mpg=49.6, engineSize=1.0),\n",
              " Row(model=' A1', year=2016, price=13900, transmission='Automatic', mileage=32260, fuelType='Petrol', tax=30, mpg=58.9, engineSize=1.4),\n",
              " Row(model=' A6', year=2016, price=13250, transmission='Automatic', mileage=76788, fuelType='Diesel', tax=30, mpg=61.4, engineSize=2.0),\n",
              " Row(model=' A4', year=2016, price=11750, transmission='Manual', mileage=75185, fuelType='Diesel', tax=20, mpg=70.6, engineSize=2.0),\n",
              " Row(model=' A3', year=2015, price=10200, transmission='Manual', mileage=46112, fuelType='Petrol', tax=20, mpg=60.1, engineSize=1.4),\n",
              " Row(model=' A1', year=2016, price=12000, transmission='Manual', mileage=22451, fuelType='Petrol', tax=30, mpg=55.4, engineSize=1.4)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7eS41QJkOU6"
      },
      "source": [
        "**Performing Data Visualization**\n",
        "* Here we perform data visualization using matplotlib plotting functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ7UPYuWkgyS"
      },
      "source": [
        "# Performing data visualization\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "eVDYH9X7hwgt",
        "outputId": "0a53304b-f3a5-40ae-8f38-13b602bb3648"
      },
      "source": [
        "# Relationship between year and price\n",
        "x1 = df.toPandas()[\"year\"].values.tolist()\n",
        "y1 = df.toPandas()[\"price\"].values.tolist()\n",
        "plt.scatter(x1,y1)\n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9a9823a7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Pueg0a5blOpf",
        "outputId": "45da899f-a804-481d-b229-da31255aebca"
      },
      "source": [
        "# Relationship between model and price\n",
        "x2 = df.toPandas()[\"model\"].values.tolist()\n",
        "y2 = df.toPandas()[\"price\"].values.tolist()\n",
        "ax = plt.subplot()\n",
        "ax.barh(x2,y2)\n",
        "ax.set_xlabel(\"Price\")\n",
        "ax.set_ylabel(\"Model\")"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Model')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dj40wme56SYuBmbUu6uyub6LkOTwBXCzp2L4P1H6LbSkjmz7Qv8J6TKYKj4jogKHuJOYCc+rXJ4B7gfvq9twRnG9/YHtgHnBaX6Htl2zfYPsLlH6HI1o/VBciuqh/eUREdNawkukkfQe43PY/1fczgcNGckLbL0o6CbhT0heBicAK2/fVQ6YBD9Z+iJ3qdOIC3gf8aqj6MwQ2ImL0DHcI7D62P9L3xvbVkr460pPaXiLpQuBE4J+AsyVtScmHWEB59CTgPEkT6vYdwMdHes6IiFhzww0Sj0o6hVVTYnyIkc3Ier2k1jyJBwEkXUXJk4AyH9ROtpdKeivwRcossdOBYykjrdpKnkTEuis5NL1nuEFiFvAFVmVH/2stW1P71z/+p1HyJD7SL0/ieUnbAH2JescCU4DX2V4h6dUjOGdERIzQsIKE7f8EPlUn3nMdkbQ2bgb+qG6/LE+i5biPAx+0vaLue3wtzxsREWtguBP8vbFOyXEX8AtJcyXtvhbnHW6exE7AB2oOxNWSdmnTvuRJRER0wHAzrr8NfMb29nWm1s8yjMn2BrCmeRLjgedszwC+A3xvoEqTJxER0RnDDRKb2b6+743tG4DNRnC+Nc2TeAS4rG5fDuwxgnNGRMQIDbfj+v66lsT36/sPA/eP5ITDzZOo2z+iBJZFwDsoyXyDSp5ERMToGe6dxPGUP+aX1dfEWjYitpdQHjd9BvhLYL6k30j6NbAncKqk/SnrXZ8p6VngZ8AFIz1nRESsOdnNTaxaFxW6y/ap9f1pwDTbh/Y7bmtKkt1k288MVuf4Sbt40jFndajFEeum5B/EUCTNrf2/qxn0cZOkKwbbb/t9a9GgnSkd1q2T9p0OLJS0q+17WsrfD1w9VICIiIjRNVSfxL6UBYAuBG6hTI8xWnYD5tXJ+4DSgV2H2r4eaA0SRwF/NYrnjoiIYRgqSLwWOIiSXf1B4CrgQtu/6HTD+kiaBLwR+Mkgx5xAGULLuAkTu9SyiIj136Ad13Vo6jW2jwH2ofQL3CBpNJYRvRuYJmllG+r2nsBtLcf9HmUG2hcGaWfyJCIiOmDI0U2Sxkv6XcrkfieyainTtWJ7AXA7da3r6hTgOtsPtZTNoibeRUREdw06uknS+cDulOm8L7J916ievEwPfjawHzAB+Dfgd/r6KSRNBf4vMKVv/qahzJgxw3PmzBnNZkZErPfajW4aKkisAJ6ub1sPFGWivwmj1LhbKEFiZ+AZSvLcOMoMsA9Q+kZeokzbAbC37d8MVFeGwK7/MpwzYvSNaAis7eEm260V228GqHM2zbC9Wp+HpFOB5bbP6EZ7IiKi6EoQiIiIddN6ESQyVXhERGesF0EiQ2AjIjpjvQgSERHRGcOdKnydkanCIyJGT+4kIiKirZ64k5A0jZJ9PbNv+KukXYGLWw7bUdKLtgdNgrhz8VNMPfmqzjU2GpHciIhm9ESQoEy9cVP9eg1AnSp8GoCkccBiRmE6kIiIGL7Gg4QkAUdSZpu9UdLGtp/rd9iBwELbD76sgoiI6Jhe6JPYD1hkeyFwA2XJ0v6OYpBJ/pInERHRGb0QJGYBF9Xti+r7lSRtBLwP+GG7CpInERHRGY0+bqp9DUcAh0qaTZk48FWSNre9rB42E7jN9mNNtTMiYqxquk/iQGC+7YP7CiSdBxwOnF+L1mg9ieRJRESMnqaDxNHAFpIWUO4ifg5cCRwtaT7wbeBNwO6SnrF9cfuqigyB7awMRY0YW5ruk9gYuMb2zrZ3oiyPepTtmZR1JT5sewPgt4Gz6iJFERHRJY3dSUjamXKX8IGW4tOBhZJ2rXkSANh+VNLjwETgye62NCJi7GryTmI3YF7fUqUAdft24PWtB0raG9gIWNjVFkZEjHFNP24akqRJwPeB49qtc508iYiIzmgySNwNTJO0sg11e0/gtvp+AnAVMNv2z9tVlDyJiIjOaCxI2F5AebR0SkvxKcB1th+qSXSXA+fbvqSJNkZEjHVND4E9Hjhb0kJgAvBvwO/Ufb8HvJ2SXHdsLTvW9rzBKkyeRETE6JHtptuApOOBPwamAkuAT9n+ccv+zwJnABNtLx2srvGTdvGkYwadTTy6LLkVEb1P0lzbM/qXN30ngaTJwGxguu2nJL2SMtS1b/8USp7EQw01MSJizOqF0U2vBpYBywFsL7e9qGX/mcDngOZveSIixpheCBJ3AI8BiySdK6mvTwJJhwKLbd8xWAUZAhsR0RmNB4maQHcI8H7gXuBMSadK2hT4E+DPhlFHhsBGRHRA430SAC6957cCt0r6KXAucCmwA3BHWbyOycBtkva2/e+NNTYiYgxpPEhI2hZ4re3batE04EHbd1L6K/qOewCYMdTopgyBjYgYPY0HCWBD4IwaLJ4DngA+1myTIiICeiBI2H5Q0mco2dczbV/Tt6/ePSwDXgKWDnUXAVlPIlaXHI2ItdN4kKhmATfVr9f027f/cIJDRESMvsaDhEqv9JHAQcCNkja2/VzDzYqICHpgCCywH7DI9kLgBqD1+YCBayXNlXRCuwqSJxER0Rm9ECRmARfV7Yvq+z5vtT0dmAmcKOntA1WQPImIiM5o9HGTpHHAEcChkmYDosz6urntZbYXA9h+XNLlwN7AvzbX4oiIsaXpPokDgfm2D+4rkHQecLikS4FX2F4maTPKJH+nD1Vh8iQiIkZP00HiaGALSQsodxE/B66s5TcBCyX1dWI/0Do8tp0MgY3RlmG0MZY13SexMXCN7Z1t7wQsAI6yPdP2/cDTtjepr9c329SIiLGnsTsJSTsDbwI+0FJ8OuXuYVfb9zTTsoiI6NPkncRuwLw6CyywckbY24G+u4aN69DWn0s6rIlGRkSMZU33SQxle9uLJe0I/EzSnTWfYjU1h+IEgHETJvbfHRERI9TkncTdwDRJK9tQt/cEbgNoGQJ7PyXRbq+BKkqeREREZzQWJGwvoDxaOqWl+BTgOtsPSdpK0ngASdsAb6EEloiI6JKmHzcdD5wtaSEwAfg3oG/50tcD35a0ghLMvmx7yCCRPImIiNGjsihcgw2QtgDOBt4BbEvJj/hd27+WtD1wOSVIbAicbftbg9U3ftIunnTMWR1udbSTnIKIdZOkubZn9C9vOk8C4LvA/ba3t70hZdqNv6v7lgD72p4GvBk4uS5OFBERXdD03E1rkisxnt4IahERY0bTf3SHzJWQNEXSfOBh4Cu2H+1fSaYKj4jojKaDxJBsP2x7D2Bn4BhJrxngmAyBjYjogKaDxJC5En3qHcRdwNu62sKIiDGs0T4J2wsk9eVK9E0D3porMRn4D9vPStoKeCtw5mB1ZghsRMToaTpPAobOlfhLSaZMJX6G7TubaWZExNjTeJ4EgKRplM7qjwAnA38E/IwyHHY8JZhdYvsLQ9WVPIm1l1yHiLGnXZ5EL9xJQFnX+ibgbbZ3BpAk4ADbyyVtCNwk6WrbP2+yoRERY0njQaIGgyOBg4AbJW1s+zmXW5zl9bAN66v5256IiDGk6dFNAPsBi+oU4DcAK591SBonaR7wOPBT27cMVEHyJCIiOqMXgsQs4KK6fVF9D5TEujolx2Rgb0m7D1RB8iQiIjqj6Wk5xgFHAIdKmk0ZwfQqSZvbXtZ3nO0nJV0PHELJlYiIiC5ouk/iQGC+7YP7CiSdBxwu6WrghRogNqH0WXxlqAqTJxERMXqaDhKzgMtbhsDOBC4FPg7MA86rdxu7AA/ZvnKoCu9c/BRTT76qg02Obsgw3Ije0HTG9XEAkr5CGQI7y/YxwBX1kL0kfQaYQUm0i4iILmq847plCOyxwEGSNm7ZN5ky2ulvm2ldRMTY1niQYJAhsMBZwOeAFQ20KyJizOuFIDHgEFhJ7wUetz13qAqSJxER0Rk9OwQWeAvwPknvBjYGJkj6ge0P96/H9jnAOVDmburaPyAiYj3X9J1E3xDYKban2t6eMrrpcNuftz3Z9lTgKOBnAwWIiIjonJ4YAtuvrG8I7PkjqTB5EhERo6fXpgqfafualvItKSObdqdM7ne87ZsHqytThUer5FtEDE+7qcKbftzUp2+q8Fn9yr8OXGP7dZQlTX/Z7YZFRIxlTT9uajtVuKQtgLdT8iew/RvgN401NCJiDOqFO4l2eRI7AE8A50q6XdLfStpsoAoyBDYiojN6IUi0myp8A2A68De29wKepixt+jKZKjwiojN6OU/iEeCRloWGLqFNkIiIiM5ouk+i7VThts+X9LCkXW3fU4+9e6gKMwQ2ImL0NP24qV2eRN8jp08CF0iaD0wD/qKLbYuIGPN6PU/ie0DfHE4DLl3aX/IkVpc8gYgYjnU1T+LvKEuWRkREA5ruk2ibJwFg+18lTW2weRERY1ov3EkMtp7EsCRPIiKiM3ohSLTLkxi25ElERHRGz+ZJ2F7WZNsiIqL5Pom2eRJkqvCIiMY1/bhp0DwJSRcCNwO7SnpE0u93uX0REWNao3cSto8boOwK4Iq6vcb9ExERMXqavpOIiIgeliARERFtJUhERERbCRIREdFWgkRERLSVIBEREW0lSERERFs9sZ7EaJK0DLin6XasgW2ApU03Yg2kvZ2V9nZW2tve9rYn9i9selqOTrhnoIUzepWkOWlv56S9nZX2dlYvtDePmyIioq0EiYiIaGt9DBLnNN2ANZT2dlba21lpb2c13t71ruM6IiJGz/p4JxEREaMkQSIiItpab4KEpEMk3SNpgaSTu3zuKZKul3S3pF9I+lQt31rSTyXdV79uVcsl6Ru1rfMlTW+p65h6/H2Sjmkpf5OkO+tnviFJa9nmcZJul3Rlfb+DpFtq/RdL2qiWj6/vF9T9U1vq+Hwtv0dS6+qCo/69kLSlpEsk/UrSLyXt2+PX99P1Z+EuSRdK2riXrrGk70l6XNJdLWUdv57tzjHC9n6t/jzMl3S5pC1Het1G8r1Z0/a27PusJEvapleu76Bsr/MvYBywENgR2Ai4A9iti+efBEyv25sD9wK7AV8FTq7lJwNfqdvvBq6mrOm9D3BLLd8auL9+3apub1X33VqPVf3szLVs82eAvweurO//ATiqbn8L+Hjd/kPgW3X7KODiur1bvc7jgR3q9R/Xqe8FcB7wB3V7I2DLXr2+wHbAImCTlmt7bC9dY+DtwHTgrpayjl/PducYYXt/G9igbn+lpb1rfN3W9HszkvbW8inAT4AHgW165foO+m9Z2wp64QXsC/yk5f3ngc832J4fAwdRMr8n1bJJlEQ/gG8Ds1qOv6funwV8u6X827VsEvCrlvLVjhtB+yYD1wEHAFfWH7SlLb9wK69n/YHet25vUI9T/2vcd1wnvhfAFpQ/uupX3qvXdzvg4frLvUG9xgf32jUGprL6H92OX8925xhJe/vtOxy4YKDrMdR1G8nP/0jbC1wC7Ak8wKog0RPXt91rfXnc1PdL2eeRWtZ19XZ0L+AW4DW2l9Rd/w68pm63a+9g5Y8MUD5SZwGfA1bU968CnrT94gD1r2xT3f9UPX5N/w1rYwfgCeBclUdkfytpM3r0+tpeDJwBPAQsoVyzufT2NYbuXM9251hbx1P+Rz2S9o7k53+NSToUWGz7jn67evr6ri9BoidIeiVwKXCS7f9q3ecS2hsfbyzpvcDjtuc23ZY1sAHl1v1vbO8FPE25lV6pV64vQH0OfCgluG0LbAYc0mij1lA3rudonUPSbOBF4IK1blSHSNoU+BPgz7p1ztG6vutLkFhMedbXZ3It6xpJG1ICxAW2L6vFj0maVPdPAh6v5e3aO1j55AHKR+ItwPskPQBcRHnk9HVgS0l9c3m11r+yTXX/FsB/jODfsDYeAR6xfUt9fwklaPTi9QV4F7DI9hO2XwAuo1z3Xr7G0J3r2e4cIyLpWOC9wIfqH8WRtPc/WPPvzZraifKfhjvq795k4DZJrx1Be7t2fYH1pk9iA0qnzg6s6pB6QxfPL+B84Kx+5V9j9U6kr9bt97B6R9WttXxryrP3reprEbB13de/o+rdo9Dud7Kq4/qHrN5x94d1+0RW77j7h7r9BlbvHLyf0jHYke8FcCOwa90+tV7bnry+wJuBXwCb1vrOAz7Za9eYl/dJdPx6tjvHCNt7CHA3MLHfcWt83db0ezOS9szFl3MAAAI6SURBVPbb9wCr+iR64vq2/XesbQW98qKMELiXMnphdpfP/VbKbd18YF59vZvy7PI64D7gn1u+wQL+T23rncCMlrqOBxbU13Et5TOAu+pn/pphdp4N0e53sipI7Fh/8BbUX5jxtXzj+n5B3b9jy+dn1/bcQ8tooE58L4BpwJx6jX9Uf2l69voCpwG/qnV+n/IHq2euMXAhpb/kBcqd2u9343q2O8cI27uA8sy+73fuWyO9biP53qxpe/vtf4BVQaLx6zvYK9NyREREW+tLn0RERHRAgkRERLSVIBEREW0lSERERFsJEhER0VaCRMRakPSSpHkqs73+sGbWDnTc/+t22yJGQ4JExNp51vY027sDvwE+1rqzL4vX9n5NNC5ibSVIRIyeG4GdJb1T0o2SrqBkBCNped9Bkv64rgVwh6Qv17KdJF0jaW797Oua+SdErG6DoQ+JiKHUO4aZwDW1aDqwu+1F/Y6bSZn87822n5G0dd11DvAx2/dJejPwTcq8WhGNSpCIWDubSJpXt28EvgvsR5l/Z9EAx78LONf2MwC2/7POHrwf8EOtWhBvfGebHTE8CRIRa+dZ29NaC+of+qfXoI5XUNYzmDbkkRFdlj6JiO76KXBc3ygoSVu7rD2ySNKRtUyS9myykRF9EiQiusj2NcAVwJz6mOp/1l0fAn5f0h2UacYPbaiJEavJLLAREdFW7iQiIqKtBImIiGgrQSIiItpKkIiIiLYSJCIioq0EiYiIaCtBIiIi2vr/c6/8/+fMSicAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "0K9JnUI0m5eu",
        "outputId": "6e518963-a436-47de-b8f7-2a2b1f56f544"
      },
      "source": [
        "# Relationship between mileage and price\n",
        "y3 = df.toPandas()[\"mileage\"]\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.bar(x2,y3)\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"Mileage\")\n",
        "plt.title(label=\"Mileage Per Model\")"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Mileage Per Model')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "DyIjB6mvz2iY",
        "outputId": "5ee9070b-fe50-4c0d-da1a-5bf78abcdd3e"
      },
      "source": [
        "# Relationship between price and engine size\n",
        "x12 = df.toPandas()[\"mileage\"].values.tolist()\n",
        "y12 = df.toPandas()[\"price\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(x12,y12)\n",
        "plt.xlabel(\"Mileage of Car\")\n",
        "plt.ylabel(\"Price of Car\")\n",
        "plt.title(\"Relationship between price and mileage\")\n"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Relationship between price and mileage')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "Ke_otgoHn0s_",
        "outputId": "a1c9df81-ff62-4f22-aaca-76f108765a23"
      },
      "source": [
        "# Relationship between model and average tax per model\n",
        "x4 = df.groupBy(\"model\").mean()[[\"model\",\"avg(tax)\"]].toPandas()[\"model\"].values.tolist()\n",
        "y4 = df.groupBy(\"model\").mean()[[\"model\",\"avg(tax)\"]].toPandas()[\"avg(tax)\"].values.tolist()\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.bar(x4, y4)\n",
        "plt.xlabel(\"model\")\n",
        "plt.ylabel(\"Average Tax\")\n",
        "plt.title(\"Average Tax Per Model\")\n"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Tax Per Model')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "HIxkggYmqpUb",
        "outputId": "7f2a787a-a01e-4cbc-f349-466de3df7298"
      },
      "source": [
        "# Classifying the cars based on transmission\n",
        "x5 = df.groupBy(\"transmission\").count().toPandas()[\"transmission\"].values.tolist()\n",
        "y5 = df.groupBy(\"transmission\").count().toPandas()[\"count\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(x5, y5)\n",
        "plt.xlabel(\"Transmission Type\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Types of Transmission\")\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Types of Transmission')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "Iq18465vsFmr",
        "outputId": "4e983c03-e4ba-43b2-8aa6-c62cb55c1e49"
      },
      "source": [
        "# Relationship between transmission and price\n",
        "x6 = df.groupBy(\"transmission\").count().toPandas()[\"transmission\"].values.tolist()\n",
        "y6 = df.groupBy(\"transmission\").mean().toPandas()[\"avg(price)\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(x6, y6)\n",
        "plt.xlabel(\"Transmission Type\")\n",
        "plt.ylabel(\"Average Price\")\n",
        "plt.title(\"Average Price of each transmission\")\n"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Price of each transmission')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "RGhjGj33uu0x",
        "outputId": "601d7907-8453-494f-e4b5-f06d912c059d"
      },
      "source": [
        "# Classification of cars based on their fuel type\n",
        "x7 = df.groupBy(\"fuelType\").count().toPandas()[\"fuelType\"].values.tolist()\n",
        "y7 = df.groupBy(\"fuelType\").count().toPandas()[\"count\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(x7, y7)\n",
        "plt.xlabel(\"Fuel Type\")\n",
        "plt.ylabel(\"Number of Cars\")\n",
        "plt.title(\"Number of car available based on Fuel Type\")\n"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of car available based on Fuel Type')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "7pssH1vIvMKz",
        "outputId": "2d26dfb5-1628-47bc-bfb4-d2f564cc35ef"
      },
      "source": [
        "# Relationship between fuel type and price\n",
        "x8 = df.groupBy(\"fuelType\").count().toPandas()[\"fuelType\"].values.tolist()\n",
        "y8 = df.groupBy(\"fuelType\").mean().toPandas()[\"avg(price)\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(x8, y8)\n",
        "plt.xlabel(\"Fuel Type\")\n",
        "plt.ylabel(\"Average Price of Cars\")\n",
        "plt.title(\"Average Price of car based on Fuel Type\")\n"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Price of car based on Fuel Type')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "uUEQm7VuvfB1",
        "outputId": "579021a3-0e3f-48c1-d9c4-05eac86e5243"
      },
      "source": [
        "# Relationship between price and engine size\n",
        "x9 = df.groupBy(\"engineSize\").mean().toPandas()[\"engineSize\"].values.tolist()\n",
        "y9 = df.groupBy(\"engineSize\").mean().toPandas()[\"avg(price)\"].values.tolist()\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.bar(x9,y9,width=0.05)\n",
        "plt.xlabel(\"Engine Size\")\n",
        "plt.ylabel(\"Average Price\")\n",
        "plt.title(\"Average Price based on Size of Engine\")\n"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Price based on Size of Engine')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "B2o-tJ_zy2pC",
        "outputId": "3e80406f-fdab-4a19-b994-349e68704502"
      },
      "source": [
        "# Relationship between price and miles per gallon (mpg)\n",
        "x10 = df.groupBy(\"mpg\").mean().toPandas()[\"mpg\"].values.tolist()\n",
        "y10 = df.groupBy(\"mpg\").mean().toPandas()[\"avg(price)\"].values.tolist()\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.bar(x10,y10,width=0.5)\n",
        "plt.xlabel(\"Mpg(Miles-Per-Gallon)\")\n",
        "plt.ylabel(\"Average Price\")\n",
        "plt.title(\"Average Price based on MPG(Miles-Per-Gallon)\")\n"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average Price based on MPG(Miles-Per-Gallon)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbx5H-eOUWq_"
      },
      "source": [
        "**Feature Engineering(DataTransformation)**\n",
        "* Performing oneHotEncoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DVUixgR0uVU"
      },
      "source": [
        "# converting categorical data in model column using the one hot encoder \n",
        "from pyspark.ml.feature import (VectorAssembler, VectorIndexer,OneHotEncoder,StringIndexer)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpYaaW9P2hF1"
      },
      "source": [
        "# Performing one-hot-encoding on model column\n",
        "model_indexer = StringIndexer(inputCol='model', outputCol='modelIndex')\n",
        "model_encoder = OneHotEncoder(inputCol='modelIndex', outputCol='modelVector')"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nb0lp6Kl3C53"
      },
      "source": [
        "# Performing one-hot-encoding on transmission column\n",
        "transmission_indexer = StringIndexer(inputCol='transmission', outputCol='transmissionIndex')\n",
        "transmission_encoder = OneHotEncoder(inputCol='transmissionIndex', outputCol='transmissionVec')"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjXflf-p3hRX"
      },
      "source": [
        "# Performing one-hot-encoding on fuel column\n",
        "fuel_type_indexer = StringIndexer(inputCol='fuelType', outputCol='fuelTypeIndex')\n",
        "fuel_type_encoder = OneHotEncoder(inputCol='fuelTypeIndex', outputCol='fuelTypeVector')"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBXfrHok3wGV"
      },
      "source": [
        "# Performing VectorAssembling on all columns except our target column\n",
        "assembler = VectorAssembler(inputCols=['modelVector', 'year', 'transmissionVec'\n",
        "            ,'mileage','fuelTypeVector','tax','mpg','engineSize'],outputCol='features')"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTEssUfWUrhL"
      },
      "source": [
        "**Building Regression Model**\n",
        "* Using multiple regression algorithms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yylx5UHR4WZe"
      },
      "source": [
        "from pyspark.ml.regression import LinearRegression"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHShckxV4jRH"
      },
      "source": [
        "linear_reg = LinearRegression(featuresCol='features',labelCol='price', maxIter=10)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCePxoXNI4gp"
      },
      "source": [
        "from pyspark.ml.regression import LinearRegressionModel\n",
        "linear_reg_model = LinearRegressionModel()"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCFpGjBg7s6u"
      },
      "source": [
        "# Splitting the dataset into train and test \n",
        "trainData, testData = df.randomSplit([0.7,.03])"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWpyDoXfA53Z"
      },
      "source": [
        "# Creating the pipeline for the model with linear regression algorithm\n",
        "from pyspark.ml import Pipeline\n",
        "pipeline = Pipeline(stages=[model_indexer,transmission_indexer,fuel_type_indexer,\n",
        "                            model_encoder,transmission_encoder,fuel_type_encoder,\n",
        "                            assembler,linear_reg])\n"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_f_eD1gBmtS"
      },
      "source": [
        "linear_fit = pipeline.fit(trainData)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SS42c0lPCZcS"
      },
      "source": [
        "results = linear_fit.transform(testData)\n",
        "# linear_fit.predict(testData)"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tlBPeu6DhWw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f77f080-2b47-4e89-85ee-f255a831bcc1"
      },
      "source": [
        "results.head(5)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(model=' A1', year=2013, price=8998, transmission='Manual', mileage=53529, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2013.0, 26: 1.0, 28: 53529.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=5496.4514918597415),\n",
              " Row(model=' A1', year=2014, price=8275, transmission='Manual', mileage=60000, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 60000.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=6810.087066974025),\n",
              " Row(model=' A1', year=2014, price=9782, transmission='Manual', mileage=27260, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 27260.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=9382.19734661933),\n",
              " Row(model=' A1', year=2014, price=10700, transmission='Manual', mileage=35700, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 35700.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=8719.136541481595),\n",
              " Row(model=' A1', year=2014, price=13750, transmission='Semi-Auto', mileage=24900, fuelType='Petrol', tax=20, mpg=60.1, engineSize=1.4, modelIndex=3.0, transmissionIndex=1.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {1: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 27: 1.0, 28: 24900.0, 30: 1.0, 31: 20.0, 32: 60.1, 33: 1.4}), prediction=11986.4677021862)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXtvo1pADsgg"
      },
      "source": [
        "# Checking out a binaryclassification evalutor\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "my_eval = BinaryClassificationEvaluator(rawPredictionCol='prediction',\n",
        "                                       labelCol='price')"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PR1FV4lExXS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae3dad31-2e38-4de6-f820-396ba4b74b94"
      },
      "source": [
        "results.select('price','prediction').show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----+------------------+\n",
            "|price|        prediction|\n",
            "+-----+------------------+\n",
            "| 8998|5496.4514918597415|\n",
            "| 8275| 6810.087066974025|\n",
            "| 9782|  9382.19734661933|\n",
            "|10700| 8719.136541481595|\n",
            "|13750|  11986.4677021862|\n",
            "| 9490|12180.394068222027|\n",
            "|10899| 8898.238263068255|\n",
            "|10965|11560.456331003457|\n",
            "|11217| 10947.51789478492|\n",
            "|11750|11513.319306941703|\n",
            "| 8500| 6419.935937521048|\n",
            "|10999|11020.805904565845|\n",
            "|11599|10923.456541713327|\n",
            "|11899|15718.025691186544|\n",
            "|11990|10164.241810647305|\n",
            "|12046|11793.648867157754|\n",
            "|13888|15469.385198357515|\n",
            "|14671| 15302.59197063744|\n",
            "|10175|12494.381706498563|\n",
            "|11175|15931.483124695718|\n",
            "+-----+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "gf2XXhBDBQq2",
        "outputId": "0af4d9cf-9154-4a43-9c0b-b535ca2b52c3"
      },
      "source": [
        "# Plotting the predicted value by linear regression against the original value\n",
        "graph_res = results.select('price', 'prediction').toPandas()\n",
        "x13 = graph_res[\"price\"]\n",
        "x14 = graph_res[\"prediction\"]\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(np.arange(0,len(x13),1),x13,label='Original Price')\n",
        "plt.scatter(np.arange(0,len(x14),1),x14, label=\"Predicted Price\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9a9e4d00d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcCDA2vQF2cM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b1a5959-daa7-4c25-8cd3-c5b11af4adeb"
      },
      "source": [
        "my_eval.evaluate(results)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHiYHi5DEyxA"
      },
      "source": [
        "# Analyzing the performance of linear regression using RegressionEvaluator\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "my_ref_eval = RegressionEvaluator(predictionCol='prediction',labelCol='price')"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzXYQzrWFAVQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b2e240c-fe31-4678-d965-dd372b228bf8"
      },
      "source": [
        "my_ref_eval.evaluate(results)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4374.016810939943"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IWaOA8eFOSa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd0f5d8a-92a5-4050-c6cf-3b252a9ee752"
      },
      "source": [
        "# R2 score\n",
        "my_ref_eval.evaluate(results, {my_ref_eval.metricName:\"r2\"})"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8519464196799553"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiXUGqj4Gu2j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b42d9f3f-7749-4de2-8fc7-7b7cd43ef21c"
      },
      "source": [
        "# RMSE score\n",
        "my_ref_eval.evaluate(results, {my_ref_eval.metricName:\"rmse\"})"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4374.016810939943"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5__yJMcGx--",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01243ea1-68ca-444c-fb8a-57f6b850695b"
      },
      "source": [
        "# MSE value\n",
        "my_ref_eval.evaluate(results, {my_ref_eval.metricName:\"mse\"})"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19132023.06238523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2ltb1-jPhMM"
      },
      "source": [
        "**RandomForest Regression Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2XXUWyVHP8T"
      },
      "source": [
        "from pyspark.ml.regression import RandomForestRegressor"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQ94wZ52Pnjx"
      },
      "source": [
        "randomforest = RandomForestRegressor(featuresCol='features',labelCol='price')"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnfJo2IhQJwM"
      },
      "source": [
        "# Creating a new pipeline with random Forest Regressor \n",
        "from pyspark.ml import Pipeline\n",
        "pipeline1 = Pipeline(stages=[model_indexer,transmission_indexer,fuel_type_indexer,\n",
        "                            model_encoder,transmission_encoder,fuel_type_encoder,\n",
        "                            assembler,randomforest])\n",
        "# df.na.drop()"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAe444j6QL4M"
      },
      "source": [
        "# Executing the random forest regression algorithm\n",
        "randomforestresult = pipeline1.fit(trainData)\n",
        "randomFResult = randomforestresult.transform(testData)"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu3Bo1cIQTMX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7926949-b45b-4d88-fd60-2ca1c37973f1"
      },
      "source": [
        "print(randomFResult.head(5))"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Row(model=' A1', year=2013, price=8998, transmission='Manual', mileage=53529, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2013.0, 26: 1.0, 28: 53529.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=10947.494916604892), Row(model=' A1', year=2014, price=8275, transmission='Manual', mileage=60000, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 60000.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=10992.640203337249), Row(model=' A1', year=2014, price=9782, transmission='Manual', mileage=27260, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 27260.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=11975.60873331797), Row(model=' A1', year=2014, price=10700, transmission='Manual', mileage=35700, fuelType='Petrol', tax=125, mpg=52.3, engineSize=1.4, modelIndex=3.0, transmissionIndex=0.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {0: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 26: 1.0, 28: 35700.0, 30: 1.0, 31: 125.0, 32: 52.3, 33: 1.4}), prediction=11975.60873331797), Row(model=' A1', year=2014, price=13750, transmission='Semi-Auto', mileage=24900, fuelType='Petrol', tax=20, mpg=60.1, engineSize=1.4, modelIndex=3.0, transmissionIndex=1.0, fuelTypeIndex=1.0, modelVector=SparseVector(25, {3: 1.0}), transmissionVec=SparseVector(2, {1: 1.0}), fuelTypeVector=SparseVector(2, {1: 1.0}), features=SparseVector(34, {3: 1.0, 25: 2014.0, 27: 1.0, 28: 24900.0, 30: 1.0, 31: 20.0, 32: 60.1, 33: 1.4}), prediction=13390.067640510655)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "iKwv-YrjD42H",
        "outputId": "efe96662-ac57-4795-91e4-240827979983"
      },
      "source": [
        "# Plotting the predicted price value by random forest regressor against the original price\n",
        "graph_random = randomFResult.select('price', 'prediction').toPandas()\n",
        "x15 = graph_random[\"price\"]\n",
        "x16 = graph_random[\"prediction\"]\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(np.arange(0,len(x15),1),x15,marker='x',label='Original Price')\n",
        "plt.xlabel(\"Index/Each entry in the dataset\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.scatter(np.arange(0,len(x16),1),x16,marker='s', label=\"Predicted Price\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9a8844f8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yd08zPHjRw_d"
      },
      "source": [
        "# Evaluating the performance of the random forest regressor using RegressionEvaluator \n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "my_ref_eval1 = RegressionEvaluator(predictionCol='prediction',labelCol='price')"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ha4Npi8ZQlJy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa686284-c3d2-4297-b06f-0bfe86cde56b"
      },
      "source": [
        "my_ref_eval1.evaluate(randomFResult)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4154.7035968499285"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdcSt1asQoNZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "667edf24-f5a5-498a-d8d1-f531cd23cd33"
      },
      "source": [
        "# R2 Score\n",
        "my_ref_eval.evaluate(randomFResult, {my_ref_eval.metricName:\"r2\"})"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8664210238311869"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Au7BK1UASAgR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24a7c7c8-15f0-412f-c893-5169aee4ef88"
      },
      "source": [
        "# RMSE value\n",
        "my_ref_eval.evaluate(randomFResult, {my_ref_eval.metricName:\"rmse\"})"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4154.7035968499285"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9QPFWRuSGIG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "201c97c7-a62e-460d-df42-120fb9bc8852"
      },
      "source": [
        "# MSE value\n",
        "my_ref_eval.evaluate(randomFResult, {my_ref_eval.metricName:\"mse\"})"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17261561.977677733"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbAw69OAYWdw"
      },
      "source": [
        "**Gradient Boosting Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UjxZ3g7SKSC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1040e77-2be4-47d0-bc2c-e17534b907f3"
      },
      "source": [
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.regression import GBTRegressor\n",
        "from pyspark.ml.feature import VectorIndexer\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "\n",
        "\n",
        "# Train a GBT model.\n",
        "gbt = GBTRegressor(featuresCol=\"features\",labelCol=\"price\", maxIter=10)\n",
        "\n",
        "# Chain indexer and GBT in a Pipeline\n",
        "pipeline_gbt = Pipeline(stages=[model_indexer,transmission_indexer,fuel_type_indexer,\n",
        "                            model_encoder,transmission_encoder,fuel_type_encoder,\n",
        "                            assembler, gbt])\n",
        "\n",
        "# Train model.  This also runs the indexer.\n",
        "model = pipeline.fit(trainData)\n",
        "\n",
        "# Make predictions.\n",
        "predictions = model.transform(testData)\n",
        "\n",
        "# Select example rows to display.\n",
        "predictions.select(\"prediction\", \"price\", \"features\").show(5)\n"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+------------------+-----+--------------------+\n",
            "|        prediction|price|            features|\n",
            "+------------------+-----+--------------------+\n",
            "|5496.4514918597415| 8998|(34,[3,25,26,28,3...|\n",
            "| 6810.087066974025| 8275|(34,[3,25,26,28,3...|\n",
            "|  9382.19734661933| 9782|(34,[3,25,26,28,3...|\n",
            "| 8719.136541481595|10700|(34,[3,25,26,28,3...|\n",
            "|  11986.4677021862|13750|(34,[3,25,27,28,3...|\n",
            "+------------------+-----+--------------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "Fm7-J6rWGKep",
        "outputId": "5cf5af1a-9490-46ef-a89e-86bbcc97d977"
      },
      "source": [
        "# Plotting the predicted price value by GBT against the original price\n",
        "graph_gbt = predictions.select(\"prediction\", \"price\").toPandas()\n",
        "x17 = graph_gbt[\"price\"]\n",
        "x18 = graph_gbt[\"prediction\"]\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(np.arange(0,len(x17),1),x17,marker='x',label='Original Price')\n",
        "plt.xlabel(\"Index/Each entry in the dataset\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.scatter(np.arange(0,len(x18),1),x18,marker='s', label=\"Predicted Price\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9a89083b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7Bc1yQAXEJi",
        "outputId": "3482ac26-28e7-4231-e93e-6ba4a8bf3461"
      },
      "source": [
        "\n",
        "# Select (prediction, true label) and compute test error\n",
        "evaluator = RegressionEvaluator(\n",
        "    labelCol=\"price\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
        "rmse = evaluator.evaluate(predictions)\n",
        "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % rmse)\n"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Root Mean Squared Error (RMSE) on test data = 4374.02\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYbqJyi7Xwky",
        "outputId": "c36eb0d3-07ba-4cb4-e0b7-cbdd46ab27f4"
      },
      "source": [
        "evaluatorR2 = RegressionEvaluator(\n",
        "    labelCol=\"price\", predictionCol=\"prediction\", metricName=\"r2\")\n",
        "r2 = evaluatorR2.evaluate(predictions)\n",
        "print(\"R2 Score on test data= %g\" % r2)\n",
        "gbtModel = model.stages[1]\n",
        "print(gbtModel)  # summary only\n"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score on test data= 0.851946\n",
            "StringIndexerModel: uid=StringIndexer_fe9612fa6f93, handleInvalid=error\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIM6LgdnaNpm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}