Machine_Learning_with_Pyspark.ipynb

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  "metadata": {
    "colab": {
      "name": "Machine Learning with Pyspark",
      "provenance": [],
      "mount_file_id": "1UKGkLbjBFkE__TTL4k6thVysLaKM6LYZ",
      "authorship_tag": "ABX9TyOgieJY9L2KVLGwLWj8WK4v"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Pyspark in Colab**\n",
        "\n",
        "Some notes on Pyspark:\n",
        "\n",
        "To run spark in Colab, we need to first install the following dependencies in Colab environment, that is;\n",
        "\n",
        "* Apache Spark 3.0.3 \n",
        "* Hadoop 2.7, \n",
        "* Java 8 \n",
        "* Findspark (to locate the spark in the system). \n",
        "\n",
        "---\n",
        "**Reading Datasets with Spark** \n",
        "---\n",
        "\n",
        "1. **Eager Mode:** \n",
        "\n",
        "Like Python Pandas, when reading CSV files into dataframes,  all of the data is **usually loaded into memory** before the next step commences execution. Here Spark performs the operation in an eager mode.\n",
        "\n",
        "2. **Lazy Approach:**\n",
        "\n",
        "The opposite occurs using the Lazy Approach, files are usually read in the **parquet format.**\n",
        "\n",
        "**Best Practice:** It's best to avoid reading your datasets via eager operations/mode when working with Spark, except if it's a small csv dataset, however it's best to avoid the eager mode. Say you need to process large CSV files/dataset, *first transform the dataset to parquet format before executing the rest of the pipeline.*\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "**Writing Data with Spark**\n",
        "---\n",
        "\n",
        "Like reading data with Spark, **it’s also not advisable to write your data to your local storage when using PySpark. Rather, make use of a distributed file system such as Hadoop Distributed File System (HDFS) or S3.** \n",
        "\n",
        "The parquet is a good format to use for saving dataframes especially if you are going to be processing the results with Spark.\n",
        "\n",
        "HDFS file formats supported are Json, Avro and Parquet.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "**Types of Processing**\n",
        "---\n",
        "\n",
        "**Batch & Streaming** data pipelines.\n",
        "\n",
        "---\n",
        "**Data Transformation**\n",
        "---\n",
        "\n",
        "Spark has **dataframe operations** for *tasks such as adding new columns, dropping columns, joins, and analytics statistics,*.\n",
        "\n",
        "Easy way to getting started spark dataframe operations is by using **Spark SQL**. \n",
        "\n",
        "\n",
        "Spark SQL operations are performed in a lazy execution mode, this implies that the SQL steps won’t be processed until an output is required. \n",
        "\n",
        "\n",
        "---\n",
        "**Machine Learning with PySpark**\n",
        "---\n",
        "\n",
        "We use the MLIB library. **MLlib** is the preferred library because it natively operates on Spark dataframes.\n",
        "\n",
        "Setting up your dataframe with a vector of features and a label as a scalar is a required step befoe fitting to train the model."
      ],
      "metadata": {
        "id": "epvi49QWUBr0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z35IP047Se-9"
      },
      "outputs": [],
      "source": [
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "!wget -q https://downloads.apache.org/spark/spark-3.0.3/spark-3.0.3-bin-hadoop2.7.tgz\n",
        "!tar xf spark-3.0.3-bin-hadoop2.7.tgz\n",
        "!pip install -q findspark"
      ]
    },
    {
      "metadata": {
        "id": "ILheUROOhprv"
      },
      "cell_type": "markdown",
      "source": [
        "### **Set the environment path:**\n",
        "\n",
        "This will enable me to run Pyspark in my Colab environment. \n",
        "\n",
        "We can set the location of Java and Spark by running the following codes:"
      ]
    },
    {
      "metadata": {
        "id": "v1b8k_OVf2QF"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.0.3-bin-hadoop2.7\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KwrqMk3HiMiE"
      },
      "cell_type": "markdown",
      "source": [
        "**Test Installation:**\n",
        "\n",
        "Run a local spark session"
      ]
    },
    {
      "metadata": {
        "id": "9_Uz1NL4gHFx"
      },
      "cell_type": "code",
      "source": [
        "import findspark\n",
        "findspark.init()\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.master(\"local[*]\").getOrCreate() #Creating SparkSession"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Extraction**\n",
        "\n",
        "I'll load my dataset from Kaggle, some big data task would require extracting dataset from AWS S3, Databases such as Redshift, MongDB etc.\n",
        "\n",
        "Dataset Source: https://www.kaggle.com/arjunbhasin2013/ccdata "
      ],
      "metadata": {
        "id": "RQPGRfjPUwN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install kaggle #install kaggle library\n",
        "! mkdir ~/.kaggle #make a directory for Kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNBoK5WAUAuB",
        "outputId": "d239bc07-3ddc-4755-c859-ac03493b686d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.10.8)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.0.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp /content/drive/MyDrive/kaggle.json ~/.kaggle/kaggle.json #copy the “kaggle.json” file from the mounted google drive to the current instance storage"
      ],
      "metadata": {
        "id": "GPaTHJ-QWZ1Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download arjunbhasin2013/ccdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SP2C6ZTWg9i",
        "outputId": "7363fb6b-b307-4830-93e1-4a52ebf62584"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ccdata.zip to /content\n",
            "\r  0% 0.00/340k [00:00<?, ?B/s]\n",
            "\r100% 340k/340k [00:00<00:00, 11.2MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip ccdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOxTRhRcWm1d",
        "outputId": "bb184cb1-1a62-41a8-f35f-b9100e0c2794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ccdata.zip\n",
            "  inflating: CC GENERAL.csv          \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Dataset Information**\n",
        "\n",
        "The Dataset summarizes the usage behavior of about **9000 active credit card** holders during the **last 6 months**. The file is at a customer level **with 18 behavioral variables.**\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Following is the Data Dictionary for Credit Card dataset :-\n",
        "\n",
        "\n",
        "* CUSTID : Identification of Credit Card holder (Categorical)\n",
        "\n",
        "* BALANCE : Balance amount left in their account to make purchases \n",
        "\n",
        "* BALANCEFREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated)\n",
        "\n",
        "* PURCHASES : Amount of purchases made from account\n",
        "ONEOFFPURCHASES : Maximum purchase amount done in one-go\n",
        "\n",
        "* INSTALLMENTS PURCHASES : Amount of purchase done in installment\n",
        "\n",
        "* CASHADVANCE : Cash in advance given by the user\n",
        "\n",
        "* PURCHASES FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)\n",
        "\n",
        "* ONEOFF PURCHASES FREQUENCY : How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)\n",
        "\n",
        "* PURCHASES INSTALLMENTS FREQUENCY : How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)\n",
        "\n",
        "* CASHADVANCE FREQUENCY : How frequently the cash in advance being paid\n",
        "\n",
        "* CASHADVANCETRX : Number of Transactions made with \"Cash in Advanced\"\n",
        "\n",
        "* PURCHASESTRX : Numbe of purchase transactions made\n",
        "\n",
        "* CREDITLIMIT : Limit of Credit Card for user\n",
        "\n",
        "* PAYMENTS : Amount of Payment done by user\n",
        "\n",
        "* MINIMUM_PAYMENTS : Minimum amount of payments made by user\n",
        "\n",
        "* PRCFULLPAYMENT : Percent of full payment paid by user\n",
        "\n",
        "* TENURE : Tenure of credit card service for user"
      ],
      "metadata": {
        "id": "naY4NXi1O26A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Load Dataset into Google Colab**\n",
        "\n",
        "Let's load the dataset into our Colab directory in **spark dataframe format**."
      ],
      "metadata": {
        "id": "bWVf4kD8W_fj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls\n",
        "# Confirm whether the datasets are properly loaded as well as other previous installations."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aOHnY1DXhBk",
        "outputId": "c8af356a-39a7-47b1-f11d-28c713be7a5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ccdata.zip\t   drive\t spark-3.0.3-bin-hadoop2.7\n",
            "'CC GENERAL.csv'   sample_data\t spark-3.0.3-bin-hadoop2.7.tgz\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Extraction & Investigation Phase**"
      ],
      "metadata": {
        "id": "CngAHQFcNOMg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset = spark.read.csv('CC GENERAL.csv',inferSchema=True, header =True)\n",
        "\n",
        "#inferSchema = InferSchema enables us to infer automatically different data types for each column.\n",
        "\n",
        "#or \n",
        "\n",
        "# Load data from a CSV\n",
        "file_location = \"CC GENERAL.csv\" \n",
        "df = spark.read.format(\"CSV\").option(\"inferSchema\", True).option(\"header\", True).load(file_location)\n",
        "display(df.take(5))"
      ],
      "metadata": {
        "id": "IgsSlitqYinY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "8578c3a9-f74d-408b-9da9-94c6d3dd83e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[Row(CUST_ID='C10001', BALANCE=40.900749, BALANCE_FREQUENCY=0.818182, PURCHASES=95.4, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=95.4, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.166667, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.083333, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=2, CREDIT_LIMIT=1000.0, PAYMENTS=201.802084, MINIMUM_PAYMENTS=139.509787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10002', BALANCE=3202.467416, BALANCE_FREQUENCY=0.909091, PURCHASES=0.0, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=6442.945483, PURCHASES_FREQUENCY=0.0, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.25, CASH_ADVANCE_TRX=4, PURCHASES_TRX=0, CREDIT_LIMIT=7000.0, PAYMENTS=4103.032597, MINIMUM_PAYMENTS=1072.340217, PRC_FULL_PAYMENT=0.222222, TENURE=12),\n",
              " Row(CUST_ID='C10003', BALANCE=2495.148862, BALANCE_FREQUENCY=1.0, PURCHASES=773.17, ONEOFF_PURCHASES=773.17, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=1.0, ONEOFF_PURCHASES_FREQUENCY=1.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=12, CREDIT_LIMIT=7500.0, PAYMENTS=622.066742, MINIMUM_PAYMENTS=627.284787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10004', BALANCE=1666.670542, BALANCE_FREQUENCY=0.636364, PURCHASES=1499.0, ONEOFF_PURCHASES=1499.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=205.788017, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.083333, CASH_ADVANCE_TRX=1, PURCHASES_TRX=1, CREDIT_LIMIT=7500.0, PAYMENTS=0.0, MINIMUM_PAYMENTS=None, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10005', BALANCE=817.714335, BALANCE_FREQUENCY=1.0, PURCHASES=16.0, ONEOFF_PURCHASES=16.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=1, CREDIT_LIMIT=1200.0, PAYMENTS=678.334763, MINIMUM_PAYMENTS=244.791237, PRC_FULL_PAYMENT=0.0, TENURE=12)]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#txt_file = spark.read.text(\"example.txt\") #reading txt in spark\n",
        "#json_file = spark.read.json(\"sample.json\", multiLine=True) #reading json in spark "
      ],
      "metadata": {
        "id": "JUmBcuulWTxC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(df.take(8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "k5ZWOWIUkgCR",
        "outputId": "b3982abf-2720-49c9-e4b0-870480bcf404"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[Row(CUST_ID='C10001', BALANCE=40.900749, BALANCE_FREQUENCY=0.818182, PURCHASES=95.4, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=95.4, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.166667, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.083333, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=2, CREDIT_LIMIT=1000.0, PAYMENTS=201.802084, MINIMUM_PAYMENTS=139.509787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10002', BALANCE=3202.467416, BALANCE_FREQUENCY=0.909091, PURCHASES=0.0, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=6442.945483, PURCHASES_FREQUENCY=0.0, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.25, CASH_ADVANCE_TRX=4, PURCHASES_TRX=0, CREDIT_LIMIT=7000.0, PAYMENTS=4103.032597, MINIMUM_PAYMENTS=1072.340217, PRC_FULL_PAYMENT=0.222222, TENURE=12),\n",
              " Row(CUST_ID='C10003', BALANCE=2495.148862, BALANCE_FREQUENCY=1.0, PURCHASES=773.17, ONEOFF_PURCHASES=773.17, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=1.0, ONEOFF_PURCHASES_FREQUENCY=1.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=12, CREDIT_LIMIT=7500.0, PAYMENTS=622.066742, MINIMUM_PAYMENTS=627.284787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10004', BALANCE=1666.670542, BALANCE_FREQUENCY=0.636364, PURCHASES=1499.0, ONEOFF_PURCHASES=1499.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=205.788017, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.083333, CASH_ADVANCE_TRX=1, PURCHASES_TRX=1, CREDIT_LIMIT=7500.0, PAYMENTS=0.0, MINIMUM_PAYMENTS=None, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10005', BALANCE=817.714335, BALANCE_FREQUENCY=1.0, PURCHASES=16.0, ONEOFF_PURCHASES=16.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=1, CREDIT_LIMIT=1200.0, PAYMENTS=678.334763, MINIMUM_PAYMENTS=244.791237, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10006', BALANCE=1809.828751, BALANCE_FREQUENCY=1.0, PURCHASES=1333.28, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=1333.28, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.666667, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.583333, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=8, CREDIT_LIMIT=1800.0, PAYMENTS=1400.05777, MINIMUM_PAYMENTS=2407.246035, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10007', BALANCE=627.260806, BALANCE_FREQUENCY=1.0, PURCHASES=7091.01, ONEOFF_PURCHASES=6402.63, INSTALLMENTS_PURCHASES=688.38, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=1.0, ONEOFF_PURCHASES_FREQUENCY=1.0, PURCHASES_INSTALLMENTS_FREQUENCY=1.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=64, CREDIT_LIMIT=13500.0, PAYMENTS=6354.314328, MINIMUM_PAYMENTS=198.065894, PRC_FULL_PAYMENT=1.0, TENURE=12),\n",
              " Row(CUST_ID='C10008', BALANCE=1823.652743, BALANCE_FREQUENCY=1.0, PURCHASES=436.2, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=436.2, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=1.0, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=1.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=12, CREDIT_LIMIT=2300.0, PAYMENTS=679.065082, MINIMUM_PAYMENTS=532.03399, PRC_FULL_PAYMENT=0.0, TENURE=12)]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Checking Schema of PySpark DataFrames**"
      ],
      "metadata": {
        "id": "6Tq9jOQNXu4m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.printSchema() # to print data types for the columns\n",
        "#txt_file.printSchema()\n",
        "#json_file.printSchema()"
      ],
      "metadata": {
        "id": "d4ipS73akQ2K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ff28ab4-8158-4502-832b-7c9275b11a7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CUST_ID: string (nullable = true)\n",
            " |-- BALANCE: double (nullable = true)\n",
            " |-- BALANCE_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES: double (nullable = true)\n",
            " |-- INSTALLMENTS_PURCHASES: double (nullable = true)\n",
            " |-- CASH_ADVANCE: double (nullable = true)\n",
            " |-- PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_TRX: integer (nullable = true)\n",
            " |-- PURCHASES_TRX: integer (nullable = true)\n",
            " |-- CREDIT_LIMIT: double (nullable = true)\n",
            " |-- PAYMENTS: double (nullable = true)\n",
            " |-- MINIMUM_PAYMENTS: double (nullable = true)\n",
            " |-- PRC_FULL_PAYMENT: double (nullable = true)\n",
            " |-- TENURE: integer (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Checking DataTypes of PySpark DataFrames**"
      ],
      "metadata": {
        "id": "itazozXWXDvf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(df)) #csv file\n",
        "#print(type(txt_file))\n",
        "#print(type(json_file))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0QgXuIcW8FD",
        "outputId": "f4e53387-d831-480f-8517-9a6f1e0fd982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pyspark.sql.dataframe.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Reading Multiple Files as Once**"
      ],
      "metadata": {
        "id": "Sq5HhtOrYOvU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#files = ['Fish.csv', 'Salary.csv']\n",
        "#df = spark.read.csv(files, sep = ',' ,  inferSchema=True, header=True)"
      ],
      "metadata": {
        "id": "161OkcGGYhME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Writing Data -Saving into Parquet**\n",
        "\n",
        "Remember, if we are going to be processing the results with Spark, then parquet is a good format to use for saving dataframes.\n",
        "\n",
        "**To save into Parquet**\n",
        "\n",
        "`f.write.save('/FileStore/parquet/file-name', format='parquet')`\n",
        "\n",
        "**To save into csv with Spark**\n",
        "\n",
        "`df.write.save('/fith-path.csv', format='csv')`"
      ],
      "metadata": {
        "id": "sijLZt8maW77"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DBFS  Databricks file store (DBFS)\n",
        "# HDFS \n",
        "df.write.save('Obi_emodi_big_data_project', format='parquet')"
      ],
      "metadata": {
        "id": "JJ5X5US0NjDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Reading Parquet**"
      ],
      "metadata": {
        "id": "lR_WwX85pgkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_test = spark.read.parquet(\"Obi_emodi_big_data_project\") #game_stats - defining new name for storage\n",
        "#https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html (spark dataframe)"
      ],
      "metadata": {
        "id": "JREm1DLJKynA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(data_test.take(5)) #parquet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "jxrQeye3QoM0",
        "outputId": "2697860f-ed85-482e-94da-7fa47660dcfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[Row(CUST_ID='C10001', BALANCE=40.900749, BALANCE_FREQUENCY=0.818182, PURCHASES=95.4, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=95.4, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.166667, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.083333, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=2, CREDIT_LIMIT=1000.0, PAYMENTS=201.802084, MINIMUM_PAYMENTS=139.509787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10002', BALANCE=3202.467416, BALANCE_FREQUENCY=0.909091, PURCHASES=0.0, ONEOFF_PURCHASES=0.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=6442.945483, PURCHASES_FREQUENCY=0.0, ONEOFF_PURCHASES_FREQUENCY=0.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.25, CASH_ADVANCE_TRX=4, PURCHASES_TRX=0, CREDIT_LIMIT=7000.0, PAYMENTS=4103.032597, MINIMUM_PAYMENTS=1072.340217, PRC_FULL_PAYMENT=0.222222, TENURE=12),\n",
              " Row(CUST_ID='C10003', BALANCE=2495.148862, BALANCE_FREQUENCY=1.0, PURCHASES=773.17, ONEOFF_PURCHASES=773.17, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=1.0, ONEOFF_PURCHASES_FREQUENCY=1.0, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=12, CREDIT_LIMIT=7500.0, PAYMENTS=622.066742, MINIMUM_PAYMENTS=627.284787, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10004', BALANCE=1666.670542, BALANCE_FREQUENCY=0.636364, PURCHASES=1499.0, ONEOFF_PURCHASES=1499.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=205.788017, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.083333, CASH_ADVANCE_TRX=1, PURCHASES_TRX=1, CREDIT_LIMIT=7500.0, PAYMENTS=0.0, MINIMUM_PAYMENTS=None, PRC_FULL_PAYMENT=0.0, TENURE=12),\n",
              " Row(CUST_ID='C10005', BALANCE=817.714335, BALANCE_FREQUENCY=1.0, PURCHASES=16.0, ONEOFF_PURCHASES=16.0, INSTALLMENTS_PURCHASES=0.0, CASH_ADVANCE=0.0, PURCHASES_FREQUENCY=0.083333, ONEOFF_PURCHASES_FREQUENCY=0.083333, PURCHASES_INSTALLMENTS_FREQUENCY=0.0, CASH_ADVANCE_FREQUENCY=0.0, CASH_ADVANCE_TRX=0, PURCHASES_TRX=1, CREDIT_LIMIT=1200.0, PAYMENTS=678.334763, MINIMUM_PAYMENTS=244.791237, PRC_FULL_PAYMENT=0.0, TENURE=12)]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Contents of PySpark DataFrame**"
      ],
      "metadata": {
        "id": "dLB2hJ7VVGzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.show() #from csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TgA5DJDVI50",
        "outputId": "e288f00b-69f6-4367-86f2-88bbfe3a8998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "| C10001|  40.900749|         0.818182|     95.4|             0.0|                  95.4|         0.0|           0.166667|                       0.0|                        0.083333|                   0.0|               0|            2|      1000.0| 201.802084|      139.509787|             0.0|    12|\n",
            "| C10002|3202.467416|         0.909091|      0.0|             0.0|                   0.0| 6442.945483|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      7000.0|4103.032597|     1072.340217|        0.222222|    12|\n",
            "| C10003|2495.148862|              1.0|   773.17|          773.17|                   0.0|         0.0|                1.0|                       1.0|                             0.0|                   0.0|               0|           12|      7500.0| 622.066742|      627.284787|             0.0|    12|\n",
            "| C10004|1666.670542|         0.636364|   1499.0|          1499.0|                   0.0|  205.788017|           0.083333|                  0.083333|                             0.0|              0.083333|               1|            1|      7500.0|        0.0|            null|             0.0|    12|\n",
            "| C10005| 817.714335|              1.0|     16.0|            16.0|                   0.0|         0.0|           0.083333|                  0.083333|                             0.0|                   0.0|               0|            1|      1200.0| 678.334763|      244.791237|             0.0|    12|\n",
            "| C10006|1809.828751|              1.0|  1333.28|             0.0|               1333.28|         0.0|           0.666667|                       0.0|                        0.583333|                   0.0|               0|            8|      1800.0| 1400.05777|     2407.246035|             0.0|    12|\n",
            "| C10007| 627.260806|              1.0|  7091.01|         6402.63|                688.38|         0.0|                1.0|                       1.0|                             1.0|                   0.0|               0|           64|     13500.0|6354.314328|      198.065894|             1.0|    12|\n",
            "| C10008|1823.652743|              1.0|    436.2|             0.0|                 436.2|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      2300.0| 679.065082|       532.03399|             0.0|    12|\n",
            "| C10009|1014.926473|              1.0|   861.49|          661.49|                 200.0|         0.0|           0.333333|                  0.083333|                            0.25|                   0.0|               0|            5|      7000.0| 688.278568|      311.963409|             0.0|    12|\n",
            "| C10010| 152.225975|         0.545455|   1281.6|          1281.6|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            3|     11000.0|1164.770591|      100.302262|             0.0|    12|\n",
            "| C10011|1293.124939|              1.0|   920.12|             0.0|                920.12|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      1200.0|1083.301007|     2172.697765|             0.0|    12|\n",
            "| C10012| 630.794744|         0.818182|  1492.18|         1492.18|                   0.0|         0.0|               0.25|                      0.25|                             0.0|                   0.0|               0|            6|      2000.0| 705.618627|      155.549069|             0.0|    12|\n",
            "| C10013| 1516.92862|              1.0|  3217.99|         2500.23|                717.76|         0.0|                1.0|                      0.25|                        0.916667|                   0.0|               0|           26|      3000.0| 608.263689|      490.207013|            0.25|    12|\n",
            "| C10014| 921.693369|              1.0|  2137.93|          419.96|               1717.97|         0.0|               0.75|                  0.166667|                            0.75|                   0.0|               0|           26|      7500.0|1655.891435|      251.137986|        0.083333|    12|\n",
            "| C10015|2772.772734|              1.0|      0.0|             0.0|                   0.0|   346.81139|                0.0|                       0.0|                             0.0|              0.083333|               1|            0|      3000.0| 805.647974|      989.962866|             0.0|    12|\n",
            "| C10016|6886.213231|              1.0|   1611.7|             0.0|                1611.7| 2301.491267|                0.5|                       0.0|                             0.5|              0.166667|               4|           11|      8000.0|1993.439277|      2109.90649|             0.0|    12|\n",
            "| C10017|2072.074354|            0.875|      0.0|             0.0|                   0.0| 2784.274703|                0.0|                       0.0|                             0.0|                  0.25|               3|            0|      3000.0| 391.974562|      376.579631|             0.0|     8|\n",
            "| C10018|  41.089489|         0.454545|    519.0|             0.0|                 519.0|         0.0|           0.416667|                       0.0|                        0.333333|                   0.0|               0|            8|      2500.0| 254.590662|       73.203221|            0.25|    12|\n",
            "| C10019|1989.072228|              1.0|   504.35|           166.0|                338.35|         0.0|           0.666667|                  0.083333|                        0.583333|                   0.0|               0|            9|     13000.0|1720.837373|      744.613395|             0.0|    12|\n",
            "| C10020|3577.970933|              1.0|   398.64|             0.0|                398.64|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      4000.0|1053.980464|     12465.54938|             0.0|    12|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.show() #parquet "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gbtka5EVZG3",
        "outputId": "b206900d-afbc-4890-d326-feb0cef458f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "| C10001|  40.900749|         0.818182|     95.4|             0.0|                  95.4|         0.0|           0.166667|                       0.0|                        0.083333|                   0.0|               0|            2|      1000.0| 201.802084|      139.509787|             0.0|    12|\n",
            "| C10002|3202.467416|         0.909091|      0.0|             0.0|                   0.0| 6442.945483|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      7000.0|4103.032597|     1072.340217|        0.222222|    12|\n",
            "| C10003|2495.148862|              1.0|   773.17|          773.17|                   0.0|         0.0|                1.0|                       1.0|                             0.0|                   0.0|               0|           12|      7500.0| 622.066742|      627.284787|             0.0|    12|\n",
            "| C10004|1666.670542|         0.636364|   1499.0|          1499.0|                   0.0|  205.788017|           0.083333|                  0.083333|                             0.0|              0.083333|               1|            1|      7500.0|        0.0|            null|             0.0|    12|\n",
            "| C10005| 817.714335|              1.0|     16.0|            16.0|                   0.0|         0.0|           0.083333|                  0.083333|                             0.0|                   0.0|               0|            1|      1200.0| 678.334763|      244.791237|             0.0|    12|\n",
            "| C10006|1809.828751|              1.0|  1333.28|             0.0|               1333.28|         0.0|           0.666667|                       0.0|                        0.583333|                   0.0|               0|            8|      1800.0| 1400.05777|     2407.246035|             0.0|    12|\n",
            "| C10007| 627.260806|              1.0|  7091.01|         6402.63|                688.38|         0.0|                1.0|                       1.0|                             1.0|                   0.0|               0|           64|     13500.0|6354.314328|      198.065894|             1.0|    12|\n",
            "| C10008|1823.652743|              1.0|    436.2|             0.0|                 436.2|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      2300.0| 679.065082|       532.03399|             0.0|    12|\n",
            "| C10009|1014.926473|              1.0|   861.49|          661.49|                 200.0|         0.0|           0.333333|                  0.083333|                            0.25|                   0.0|               0|            5|      7000.0| 688.278568|      311.963409|             0.0|    12|\n",
            "| C10010| 152.225975|         0.545455|   1281.6|          1281.6|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            3|     11000.0|1164.770591|      100.302262|             0.0|    12|\n",
            "| C10011|1293.124939|              1.0|   920.12|             0.0|                920.12|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      1200.0|1083.301007|     2172.697765|             0.0|    12|\n",
            "| C10012| 630.794744|         0.818182|  1492.18|         1492.18|                   0.0|         0.0|               0.25|                      0.25|                             0.0|                   0.0|               0|            6|      2000.0| 705.618627|      155.549069|             0.0|    12|\n",
            "| C10013| 1516.92862|              1.0|  3217.99|         2500.23|                717.76|         0.0|                1.0|                      0.25|                        0.916667|                   0.0|               0|           26|      3000.0| 608.263689|      490.207013|            0.25|    12|\n",
            "| C10014| 921.693369|              1.0|  2137.93|          419.96|               1717.97|         0.0|               0.75|                  0.166667|                            0.75|                   0.0|               0|           26|      7500.0|1655.891435|      251.137986|        0.083333|    12|\n",
            "| C10015|2772.772734|              1.0|      0.0|             0.0|                   0.0|   346.81139|                0.0|                       0.0|                             0.0|              0.083333|               1|            0|      3000.0| 805.647974|      989.962866|             0.0|    12|\n",
            "| C10016|6886.213231|              1.0|   1611.7|             0.0|                1611.7| 2301.491267|                0.5|                       0.0|                             0.5|              0.166667|               4|           11|      8000.0|1993.439277|      2109.90649|             0.0|    12|\n",
            "| C10017|2072.074354|            0.875|      0.0|             0.0|                   0.0| 2784.274703|                0.0|                       0.0|                             0.0|                  0.25|               3|            0|      3000.0| 391.974562|      376.579631|             0.0|     8|\n",
            "| C10018|  41.089489|         0.454545|    519.0|             0.0|                 519.0|         0.0|           0.416667|                       0.0|                        0.333333|                   0.0|               0|            8|      2500.0| 254.590662|       73.203221|            0.25|    12|\n",
            "| C10019|1989.072228|              1.0|   504.35|           166.0|                338.35|         0.0|           0.666667|                  0.083333|                        0.583333|                   0.0|               0|            9|     13000.0|1720.837373|      744.613395|             0.0|    12|\n",
            "| C10020|3577.970933|              1.0|   398.64|             0.0|                398.64|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      4000.0|1053.980464|     12465.54938|             0.0|    12|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print((data_test.count(), len(data_test.columns)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euTd5pnVI5lo",
        "outputId": "f50a9625-3080-4130-eaf4-5efed8d9bde1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8950, 18)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5qGEsJIJBH6",
        "outputId": "becb2a20-dc99-40ab-d0c8-b66126f504a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CUST_ID',\n",
              " 'BALANCE',\n",
              " 'BALANCE_FREQUENCY',\n",
              " 'PURCHASES',\n",
              " 'ONEOFF_PURCHASES',\n",
              " 'INSTALLMENTS_PURCHASES',\n",
              " 'CASH_ADVANCE',\n",
              " 'PURCHASES_FREQUENCY',\n",
              " 'ONEOFF_PURCHASES_FREQUENCY',\n",
              " 'PURCHASES_INSTALLMENTS_FREQUENCY',\n",
              " 'CASH_ADVANCE_FREQUENCY',\n",
              " 'CASH_ADVANCE_TRX',\n",
              " 'PURCHASES_TRX',\n",
              " 'CREDIT_LIMIT',\n",
              " 'PAYMENTS',\n",
              " 'MINIMUM_PAYMENTS',\n",
              " 'PRC_FULL_PAYMENT',\n",
              " 'TENURE']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **PySpark DataFrame to Pandas DataFrame**"
      ],
      "metadata": {
        "id": "Pnk4pBK6ZBYc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One advantage is for more EDA:\n",
        "\n",
        "If needed, we can use the toPandas() function to create a Pandas dataframe on the driver node, which means that any Python plotting library can be used for visualizing the results. However, **this approach should be used for only small dataframes, since all of the data is eagerly fetched into memory on the driver node.**"
      ],
      "metadata": {
        "id": "LjSmPpQPaleL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#df = csv_file.toPandas() #convert the PySpark DataFrame into a Pandas DataFrame\n",
        "df_topandas = data_test.toPandas() # or use df (csv)"
      ],
      "metadata": {
        "id": "u-oiQ0l7auh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_topandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "v1DUTMaebbzO",
        "outputId": "5b04731c-73df-432f-ab21-97f5cc3eea74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2d099ec4-6869-4f84-82e9-3037a4335489\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CUST_ID</th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C10001</td>\n",
              "      <td>40.900749</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>95.40</td>\n",
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              "      <td>2</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>201.802084</td>\n",
              "      <td>139.509787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C10002</td>\n",
              "      <td>3202.467416</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6442.945483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>4103.032597</td>\n",
              "      <td>1072.340217</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C10003</td>\n",
              "      <td>2495.148862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>773.17</td>\n",
              "      <td>773.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>622.066742</td>\n",
              "      <td>627.284787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C10004</td>\n",
              "      <td>1666.670542</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>205.788017</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C10005</td>\n",
              "      <td>817.714335</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>16.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>678.334763</td>\n",
              "      <td>244.791237</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8945</th>\n",
              "      <td>C19186</td>\n",
              "      <td>28.493517</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>291.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>291.12</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>325.594462</td>\n",
              "      <td>48.886365</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8946</th>\n",
              "      <td>C19187</td>\n",
              "      <td>19.183215</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>300.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>300.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>275.861322</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8947</th>\n",
              "      <td>C19188</td>\n",
              "      <td>23.398673</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>144.40</td>\n",
              "      <td>0.00</td>\n",
              "      <td>144.40</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>81.270775</td>\n",
              "      <td>82.418369</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8948</th>\n",
              "      <td>C19189</td>\n",
              "      <td>13.457564</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>36.558778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>52.549959</td>\n",
              "      <td>55.755628</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8949</th>\n",
              "      <td>C19190</td>\n",
              "      <td>372.708075</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1093.25</td>\n",
              "      <td>1093.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>127.040008</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>63.165404</td>\n",
              "      <td>88.288956</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8950 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d099ec4-6869-4f84-82e9-3037a4335489')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d099ec4-6869-4f84-82e9-3037a4335489 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2d099ec4-6869-4f84-82e9-3037a4335489');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     CUST_ID      BALANCE  ...  PRC_FULL_PAYMENT  TENURE\n",
              "0     C10001    40.900749  ...          0.000000      12\n",
              "1     C10002  3202.467416  ...          0.222222      12\n",
              "2     C10003  2495.148862  ...          0.000000      12\n",
              "3     C10004  1666.670542  ...          0.000000      12\n",
              "4     C10005   817.714335  ...          0.000000      12\n",
              "...      ...          ...  ...               ...     ...\n",
              "8945  C19186    28.493517  ...          0.500000       6\n",
              "8946  C19187    19.183215  ...          0.000000       6\n",
              "8947  C19188    23.398673  ...          0.250000       6\n",
              "8948  C19189    13.457564  ...          0.250000       6\n",
              "8949  C19190   372.708075  ...          0.000000       6\n",
              "\n",
              "[8950 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_topandas.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOUDLcrdVGEc",
        "outputId": "dd412131-ae2f-41eb-fd2f-d5f350a2cce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CUST_ID                               0\n",
              "BALANCE                               0\n",
              "BALANCE_FREQUENCY                     0\n",
              "PURCHASES                             0\n",
              "ONEOFF_PURCHASES                      0\n",
              "INSTALLMENTS_PURCHASES                0\n",
              "CASH_ADVANCE                          0\n",
              "PURCHASES_FREQUENCY                   0\n",
              "ONEOFF_PURCHASES_FREQUENCY            0\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY      0\n",
              "CASH_ADVANCE_FREQUENCY                0\n",
              "CASH_ADVANCE_TRX                      0\n",
              "PURCHASES_TRX                         0\n",
              "CREDIT_LIMIT                          1\n",
              "PAYMENTS                              0\n",
              "MINIMUM_PAYMENTS                    313\n",
              "PRC_FULL_PAYMENT                      0\n",
              "TENURE                                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df_topandas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuI4XU6CbkNc",
        "outputId": "9d1302e4-e4f0-4ee5-baca-74c42b06df20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_topandas.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "CODbMAcvqhHa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_topandas.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Suqdr5iurA82",
        "outputId": "06d8ec92-cf10-41c0-c4e8-9008663b02f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CUST_ID                             0\n",
              "BALANCE                             0\n",
              "BALANCE_FREQUENCY                   0\n",
              "PURCHASES                           0\n",
              "ONEOFF_PURCHASES                    0\n",
              "INSTALLMENTS_PURCHASES              0\n",
              "CASH_ADVANCE                        0\n",
              "PURCHASES_FREQUENCY                 0\n",
              "ONEOFF_PURCHASES_FREQUENCY          0\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY    0\n",
              "CASH_ADVANCE_FREQUENCY              0\n",
              "CASH_ADVANCE_TRX                    0\n",
              "PURCHASES_TRX                       0\n",
              "CREDIT_LIMIT                        0\n",
              "PAYMENTS                            0\n",
              "MINIMUM_PAYMENTS                    0\n",
              "PRC_FULL_PAYMENT                    0\n",
              "TENURE                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Convert Pandas DF to Spark DF**\n",
        "\n",
        "Spark provides a createDataFrame(pandas_dataframe) method to convert pandas to Spark DataFrame, "
      ],
      "metadata": {
        "id": "UE0rmugjuaoL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import SparkSession\n",
        "\n",
        "#Create PySpark SparkSession\n",
        "spark = SparkSession.builder \\\n",
        "    .master(\"local[1]\") \\\n",
        "    .appName(\"test\") \\\n",
        "    .getOrCreate()\n",
        "\n",
        "#Create PySpark DataFrame from Pandas\n",
        "sparkDF=spark.createDataFrame(df_topandas) \n",
        "sparkDF.printSchema()\n",
        "sparkDF.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVDVdxsZuuuF",
        "outputId": "28875fcf-d10b-4d7e-82df-49af5600b422"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CUST_ID: string (nullable = true)\n",
            " |-- BALANCE: double (nullable = true)\n",
            " |-- BALANCE_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES: double (nullable = true)\n",
            " |-- INSTALLMENTS_PURCHASES: double (nullable = true)\n",
            " |-- CASH_ADVANCE: double (nullable = true)\n",
            " |-- PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_TRX: long (nullable = true)\n",
            " |-- PURCHASES_TRX: long (nullable = true)\n",
            " |-- CREDIT_LIMIT: double (nullable = true)\n",
            " |-- PAYMENTS: double (nullable = true)\n",
            " |-- MINIMUM_PAYMENTS: double (nullable = true)\n",
            " |-- PRC_FULL_PAYMENT: double (nullable = true)\n",
            " |-- TENURE: long (nullable = true)\n",
            "\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "| C10001|  40.900749|         0.818182|     95.4|             0.0|                  95.4|         0.0|           0.166667|                       0.0|                        0.083333|                   0.0|               0|            2|      1000.0| 201.802084|      139.509787|             0.0|    12|\n",
            "| C10002|3202.467416|         0.909091|      0.0|             0.0|                   0.0| 6442.945483|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      7000.0|4103.032597|     1072.340217|        0.222222|    12|\n",
            "| C10003|2495.148862|              1.0|   773.17|          773.17|                   0.0|         0.0|                1.0|                       1.0|                             0.0|                   0.0|               0|           12|      7500.0| 622.066742|      627.284787|             0.0|    12|\n",
            "| C10004|1666.670542|         0.636364|   1499.0|          1499.0|                   0.0|  205.788017|           0.083333|                  0.083333|                             0.0|              0.083333|               1|            1|      7500.0|        0.0|             0.0|             0.0|    12|\n",
            "| C10005| 817.714335|              1.0|     16.0|            16.0|                   0.0|         0.0|           0.083333|                  0.083333|                             0.0|                   0.0|               0|            1|      1200.0| 678.334763|      244.791237|             0.0|    12|\n",
            "| C10006|1809.828751|              1.0|  1333.28|             0.0|               1333.28|         0.0|           0.666667|                       0.0|                        0.583333|                   0.0|               0|            8|      1800.0| 1400.05777|     2407.246035|             0.0|    12|\n",
            "| C10007| 627.260806|              1.0|  7091.01|         6402.63|                688.38|         0.0|                1.0|                       1.0|                             1.0|                   0.0|               0|           64|     13500.0|6354.314328|      198.065894|             1.0|    12|\n",
            "| C10008|1823.652743|              1.0|    436.2|             0.0|                 436.2|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      2300.0| 679.065082|       532.03399|             0.0|    12|\n",
            "| C10009|1014.926473|              1.0|   861.49|          661.49|                 200.0|         0.0|           0.333333|                  0.083333|                            0.25|                   0.0|               0|            5|      7000.0| 688.278568|      311.963409|             0.0|    12|\n",
            "| C10010| 152.225975|         0.545455|   1281.6|          1281.6|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            3|     11000.0|1164.770591|      100.302262|             0.0|    12|\n",
            "| C10011|1293.124939|              1.0|   920.12|             0.0|                920.12|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      1200.0|1083.301007|     2172.697765|             0.0|    12|\n",
            "| C10012| 630.794744|         0.818182|  1492.18|         1492.18|                   0.0|         0.0|               0.25|                      0.25|                             0.0|                   0.0|               0|            6|      2000.0| 705.618627|      155.549069|             0.0|    12|\n",
            "| C10013| 1516.92862|              1.0|  3217.99|         2500.23|                717.76|         0.0|                1.0|                      0.25|                        0.916667|                   0.0|               0|           26|      3000.0| 608.263689|      490.207013|            0.25|    12|\n",
            "| C10014| 921.693369|              1.0|  2137.93|          419.96|               1717.97|         0.0|               0.75|                  0.166667|                            0.75|                   0.0|               0|           26|      7500.0|1655.891435|      251.137986|        0.083333|    12|\n",
            "| C10015|2772.772734|              1.0|      0.0|             0.0|                   0.0|   346.81139|                0.0|                       0.0|                             0.0|              0.083333|               1|            0|      3000.0| 805.647974|      989.962866|             0.0|    12|\n",
            "| C10016|6886.213231|              1.0|   1611.7|             0.0|                1611.7| 2301.491267|                0.5|                       0.0|                             0.5|              0.166667|               4|           11|      8000.0|1993.439277|      2109.90649|             0.0|    12|\n",
            "| C10017|2072.074354|            0.875|      0.0|             0.0|                   0.0| 2784.274703|                0.0|                       0.0|                             0.0|                  0.25|               3|            0|      3000.0| 391.974562|      376.579631|             0.0|     8|\n",
            "| C10018|  41.089489|         0.454545|    519.0|             0.0|                 519.0|         0.0|           0.416667|                       0.0|                        0.333333|                   0.0|               0|            8|      2500.0| 254.590662|       73.203221|            0.25|    12|\n",
            "| C10019|1989.072228|              1.0|   504.35|           166.0|                338.35|         0.0|           0.666667|                  0.083333|                        0.583333|                   0.0|               0|            9|     13000.0|1720.837373|      744.613395|             0.0|    12|\n",
            "| C10020|3577.970933|              1.0|   398.64|             0.0|                398.64|         0.0|                1.0|                       0.0|                             1.0|                   0.0|               0|           12|      4000.0|1053.980464|     12465.54938|             0.0|    12|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Taking it back to Parquet**"
      ],
      "metadata": {
        "id": "5RbuOc-sw4Lq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# HDFS \n",
        "sparkDF.write.save('big_data_Project', format='parquet')"
      ],
      "metadata": {
        "id": "vVw1Pop-wjtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reading Parquet**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vch5XSzOw7Xs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_test = spark.read.parquet(\"big_data_Project\") #game_stats - defining new name for storage\n",
        "#https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html (spark dataframe)"
      ],
      "metadata": {
        "id": "fIyZOe9rwkeq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Machine Learning in Pyspark**\n",
        "\n",
        "Machine Learning Library (SPark) - MLlib\n",
        "\n",
        "The pandas Scikit-learn used for machine learning is great, however it does not scale to large data sets in a distributed environment.\n",
        "\n",
        "This notebook considers these ML Approaches;\n",
        "\n",
        "1. **Supervised Learning with Pyspark**: \n",
        "\n",
        "  > **Regression Model** (Linear)\n",
        "\n",
        "  > **Classification Model** (Logistic Regression)\n",
        "\n",
        "2. **Unspervised Learning with Pyspark**\n",
        "\n",
        "  > **Clustering** (Bisecting  K-means) etc\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "The steps to building and using a machine learning model are:\n",
        "\n",
        "\n",
        "* **Define/Algorithm Selection**: What type of model will it be? A linear regression? Some other type of model? Some other parameters of the model type are specified too.\n",
        "\n",
        "* **Fit:** Capture patterns from provided data. This is the heart of modeling.\n",
        "\n",
        "* **Predict:** Test your model and predict\n",
        "\n",
        "* **Evaluate:** Determine how accurate the model's predictions are.\n",
        "\n"
      ],
      "metadata": {
        "id": "DSi1aTXXnr0q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to use **one of the supervised algorithms in MLlib, we need to set up our dataframe with a vector of features and a label as a scalar.** Once done, I can use the fit function to train the model. "
      ],
      "metadata": {
        "id": "8SWrO1GapBod"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Linear Regression Model**"
      ],
      "metadata": {
        "id": "Btw9PykarK2D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#MLlib imports \n",
        "#dependencies\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml.regression import LinearRegression"
      ],
      "metadata": {
        "id": "BuAJE5NqoFNA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Handling Missing Values with Pyspark**"
      ],
      "metadata": {
        "id": "8ggpJSsVV4p4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Get count of both null and missing values in pyspark\n",
        "\n",
        "from pyspark.sql.functions import isnan, when, count, col\n",
        "\n",
        "data_test.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in data_test.columns]).show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88e8c0a6-8628-4707-d084-669b77174e44",
        "id": "7I8vnm3yjtAT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+--------+----------------+----------------+------+\n",
            "|CUST_ID|BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|\n",
            "+-------+-------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+--------+----------------+----------------+------+\n",
            "|      0|      0|                0|        0|               0|                     0|           0|                  0|                         0|                               0|                     0|               0|            0|           0|       0|               0|               0|     0|\n",
            "+-------+-------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+--------+----------------+----------------+------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test.columns #to determine X features for our linear regression prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKGW91KHSJr5",
        "outputId": "a1639de3-3359-481b-eadd-5ed2c5e0e6e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CUST_ID',\n",
              " 'BALANCE',\n",
              " 'BALANCE_FREQUENCY',\n",
              " 'PURCHASES',\n",
              " 'ONEOFF_PURCHASES',\n",
              " 'INSTALLMENTS_PURCHASES',\n",
              " 'CASH_ADVANCE',\n",
              " 'PURCHASES_FREQUENCY',\n",
              " 'ONEOFF_PURCHASES_FREQUENCY',\n",
              " 'PURCHASES_INSTALLMENTS_FREQUENCY',\n",
              " 'CASH_ADVANCE_FREQUENCY',\n",
              " 'CASH_ADVANCE_TRX',\n",
              " 'PURCHASES_TRX',\n",
              " 'CREDIT_LIMIT',\n",
              " 'PAYMENTS',\n",
              " 'MINIMUM_PAYMENTS',\n",
              " 'PRC_FULL_PAYMENT',\n",
              " 'TENURE']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Feature Selection:** \n",
        "\n",
        "`CUST_ID, BALANCE, PURCHASES, ONEOFF_PURCHASES, CREDIT_LIMIT, MINIMUM_PAYMENTS` (their index)\n",
        "\n",
        "**Target (Y)** is `Payments` (to predict payments per user)\n"
      ],
      "metadata": {
        "id": "zXI1SUAUSaYn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **PS**: **Confirm Data Types**\n",
        "\n",
        "**VectorAssembler Transformer does not take strings**\n",
        "\n",
        "**Vector Assembler** is a transformer that assembles all the features into one vector from multiple columns that contain type double. \n",
        "\n",
        "We will use a **StringIndexer** if any of the columns contains string values to convert it into *numeric values*"
      ],
      "metadata": {
        "id": "UyDCsPKWv3ZH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Using the String Indexer to Convert (String to Numeric)**\n",
        "\n",
        "\n",
        "*'The best way that I've found to do it is to combine several StringIndex on a list and use a Pipeline to execute them all':* https://stackoverflow.com/questions/36942233/apply-stringindexer-to-several-columns-in-a-pyspark-dataframe"
      ],
      "metadata": {
        "id": "jtKbK2L6TThA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.feature import StringIndexer\n",
        "\n",
        "indexers = [StringIndexer(inputCol=column, outputCol=column+\"_index\").fit(data_test) for column in list(set(data_test.columns)-set(['index'])) ]\n",
        "\n",
        "\n",
        "pipeline = Pipeline(stages=indexers)\n",
        "df_r = pipeline.fit(data_test).transform(data_test)\n",
        "\n",
        "df_r.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWw148sbUqrs",
        "outputId": "5b9ff340-13ba-4f66-bf73-cd6c509b5d4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+--------------------------------+------------------+--------------------------------------+------------------+-------------------+-------------+----------------------+----------------------+-------------+----------------------+----------------------------+---------------+-----------------------+-------------------------+--------------+----------------------+----------------------------+------------+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|ONEOFF_PURCHASES_FREQUENCY_index|CASH_ADVANCE_index|PURCHASES_INSTALLMENTS_FREQUENCY_index|CREDIT_LIMIT_index|PURCHASES_TRX_index|CUST_ID_index|CASH_ADVANCE_TRX_index|MINIMUM_PAYMENTS_index|BALANCE_index|PRC_FULL_PAYMENT_index|INSTALLMENTS_PURCHASES_index|PURCHASES_index|BALANCE_FREQUENCY_index|PURCHASES_FREQUENCY_index|PAYMENTS_index|ONEOFF_PURCHASES_index|CASH_ADVANCE_FREQUENCY_index|TENURE_index|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+--------------------------------+------------------+--------------------------------------+------------------+-------------------+-------------+----------------------+----------------------+-------------+----------------------+----------------------------+---------------+-----------------------+-------------------------+--------------+----------------------+----------------------------+------------+\n",
            "| C14212| 105.069311|              1.0|  1344.87|         1344.87|                   0.0|         0.0|           0.916667|                  0.916667|                             0.0|                   0.0|               0|           12|      1700.0|1461.200691|      176.308343|            0.25|    12|                            10.0|               0.0|                                   0.0|              24.0|                2.0|       4096.0|                   0.0|                2676.0|        293.0|                   4.0|                         0.0|         1113.0|                    0.0|                      3.0|        1433.0|                 776.0|                         0.0|         0.0|\n",
            "| C14213|2446.709596|              1.0|      0.0|             0.0|                   0.0| 5281.049743|                0.0|                       0.0|                             0.0|                  0.25|               6|            0|      5000.0|5695.396335|      696.156899|             0.0|    12|                             0.0|            3241.0|                                   0.0|               7.0|                0.0|       4097.0|                   6.0|                7459.0|       3732.0|                   0.0|                         0.0|            0.0|                    0.0|                      1.0|        6606.0|                   0.0|                         3.0|         0.0|\n",
            "| C14214| 295.374207|         0.909091|      0.0|             0.0|                   0.0| 2032.983385|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      1200.0|2781.869782|      174.631958|             0.0|    12|                             0.0|            1395.0|                                   0.0|               2.0|                0.0|       4098.0|                   4.0|                2592.0|       4459.0|                   0.0|                         0.0|            0.0|                    1.0|                      1.0|        3829.0|                   0.0|                         3.0|         0.0|\n",
            "| C14215| 822.755885|              1.0|   531.14|          359.24|                 171.9|         0.0|                1.0|                  0.428571|                        0.571429|                   0.0|               0|           10|      3000.0| 267.707871|      117.131173|             0.0|     7|                            29.0|               0.0|                                  33.0|               0.0|               12.0|       4099.0|                   0.0|                 532.0|       8133.0|                   0.0|                       151.0|         4643.0|                    0.0|                      0.0|        3675.0|                2380.0|                         0.0|         5.0|\n",
            "| C14216|4433.582508|              1.0|      0.0|             0.0|                   0.0| 1434.153185|                0.0|                       0.0|                             0.0|              0.416667|               9|            0|      5500.0|6801.553821|     1209.064809|        0.083333|    12|                             0.0|             653.0|                                   0.0|              20.0|                0.0|       4100.0|                  10.0|                 640.0|       5930.0|                   2.0|                         0.0|            0.0|                    0.0|                      1.0|        7305.0|                   0.0|                         5.0|         0.0|\n",
            "| C14217|2785.584338|              1.0|      0.0|             0.0|                   0.0|  144.330518|                0.0|                       0.0|                             0.0|                   0.1|               1|            0|      3000.0| 446.776969|       733.62973|             0.0|    10|                             0.0|             668.0|                                   0.0|               0.0|                0.0|       4101.0|                   1.0|                7621.0|       4223.0|                   0.0|                         0.0|            0.0|                    0.0|                      1.0|        5658.0|                   0.0|                        15.0|         2.0|\n",
            "| C14218|10116.70899|              1.0| 10166.69|          3300.0|               6866.69|         0.0|           0.833333|                  0.333333|                        0.666667|                   0.0|               0|           45|     14000.0|2190.466875|     4253.469898|             0.0|    12|                             5.0|               0.0|                                   7.0|              32.0|               47.0|       4102.0|                   0.0|                5996.0|        170.0|                   0.0|                      3765.0|          390.0|                    0.0|                      6.0|        2974.0|                2253.0|                         0.0|         0.0|\n",
            "| C14219|9440.796576|              1.0|      0.0|             0.0|                   0.0|    5831.378|                0.0|                       0.0|                             0.0|              0.666667|              13|            0|     13000.0|2031.387858|     2432.132089|             0.0|    12|                             0.0|            3415.0|                                   0.0|              27.0|                0.0|       4103.0|                  13.0|                4338.0|       8650.0|                   0.0|                         0.0|            0.0|                    0.0|                      1.0|        2693.0|                   0.0|                         8.0|         0.0|\n",
            "| C14220|1435.975897|              1.0|   418.56|          418.56|                   0.0|         0.0|           0.333333|                  0.333333|                             0.0|                   0.0|               0|            4|      2150.0| 950.209773|      466.163755|             0.0|    12|                             5.0|               0.0|                                   0.0|              94.0|                6.0|       4104.0|                   0.0|                6282.0|       1637.0|                   0.0|                         0.0|         4003.0|                    0.0|                      7.0|        8537.0|                2611.0|                         0.0|         0.0|\n",
            "| C14221|1160.514847|              1.0|  3726.79|           664.0|               3062.79|         0.0|                1.0|                  0.083333|                             1.0|                   0.0|               0|           89|      3000.0|3312.651656|      310.179028|        0.083333|    12|                             1.0|               0.0|                                   1.0|               0.0|              131.0|       4105.0|                   0.0|                5134.0|        761.0|                   2.0|                      2304.0|         3721.0|                    0.0|                      0.0|        4529.0|                3323.0|                         0.0|         0.0|\n",
            "| C14222| 634.759973|              1.0|    19.91|             0.0|                 19.91|         0.0|           0.083333|                       0.0|                        0.083333|                   0.0|               0|            1|      6000.0| 610.491342|      246.804668|             0.0|    12|                             0.0|               0.0|                                   9.0|               6.0|                1.0|       4106.0|                   0.0|                4369.0|       7211.0|                   0.0|                      1505.0|         1982.0|                    0.0|                      2.0|        6909.0|                   0.0|                         0.0|         0.0|\n",
            "| C14223|  45.785125|         0.636364|     12.0|             0.0|                  12.0|  166.160258|                1.0|                       0.0|                        0.916667|              0.083333|               3|           12|      1200.0|  39.556692|       46.550149|             0.0|    12|                             0.0|             927.0|                                   3.0|               2.0|                2.0|       4107.0|                   3.0|                6248.0|       5993.0|                   0.0|                       678.0|          131.0|                    5.0|                      0.0|        5147.0|                   0.0|                         1.0|         0.0|\n",
            "| C14224| 968.504914|              1.0|   257.71|           45.65|                212.06|         0.0|           0.166667|                  0.083333|                        0.166667|                   0.0|               0|            4|      1000.0| 623.314494|      842.242726|             0.0|    12|                             1.0|               0.0|                                   6.0|               3.0|                6.0|       4108.0|                   0.0|                8047.0|       8737.0|                   0.0|                      1691.0|         2755.0|                    0.0|                      5.0|        6989.0|                   1.0|                         0.0|         0.0|\n",
            "| C14225| 200.806352|              1.0|  1957.45|         1799.14|                158.31|         0.0|                1.0|                       1.0|                        0.166667|                   0.0|               0|           49|      6000.0|1934.825977|      193.934077|            0.75|    12|                             3.0|               0.0|                                   6.0|               6.0|               48.0|       4109.0|                   0.0|                3391.0|       3019.0|                  13.0|                      1159.0|         2052.0|                    0.0|                      0.0|        2509.0|                1271.0|                         0.0|         0.0|\n",
            "| C14226|1634.559235|              1.0|  1062.94|         1006.18|                 56.76|         0.0|           0.583333|                       0.5|                        0.166667|                   0.0|               0|           15|      1800.0| 584.660133|     1332.692469|             0.0|    12|                             7.0|               0.0|                                   6.0|              13.0|               17.0|       4110.0|                   0.0|                1064.0|       2199.0|                   0.0|                      3385.0|          506.0|                    0.0|                      9.0|        6718.0|                 253.0|                         0.0|         0.0|\n",
            "| C14227|1839.926709|              1.0|   487.75|             0.0|                487.75|  266.614527|                0.5|                       0.0|                        0.416667|              0.083333|               1|            9|      3500.0| 791.115227|      385.482464|             0.0|    12|                             0.0|            1878.0|                                   2.0|              14.0|               10.0|       4111.0|                   1.0|                5708.0|       2641.0|                   0.0|                      3148.0|         4417.0|                    0.0|                      4.0|        7890.0|                   0.0|                         1.0|         0.0|\n",
            "| C14228|6743.313868|              1.0|  5591.26|          1770.5|               3820.76| 1074.347669|                1.0|                       1.0|                             1.0|                  0.25|               4|          162|      9500.0|2314.208069|     1682.939354|             0.0|    12|                             3.0|             130.0|                                   1.0|              21.0|              126.0|       4112.0|                   4.0|                2301.0|       7399.0|                   0.0|                      2686.0|         4776.0|                    0.0|                      0.0|        3170.0|                1244.0|                         3.0|         0.0|\n",
            "| C14229|1267.054186|              1.0|      0.0|             0.0|                   0.0| 1253.614385|                0.0|                       0.0|                             0.0|              0.166667|               8|            0|      1500.0| 209.297618|      470.721288|             0.0|    12|                             0.0|             414.0|                                   0.0|               1.0|                0.0|       4113.0|                   8.0|                6320.0|       1117.0|                   0.0|                         0.0|            0.0|                    0.0|                      1.0|        2795.0|                   0.0|                         2.0|         0.0|\n",
            "| C14230|2778.737121|              1.0|   1973.0|          1973.0|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            2|      6500.0| 630.766685|      728.216731|             0.0|    12|                             2.0|               0.0|                                   0.0|              12.0|                3.0|       4114.0|                   0.0|                7595.0|       4217.0|                   0.0|                         0.0|          174.0|                    0.0|                      5.0|        7029.0|                1406.0|                         0.0|         0.0|\n",
            "| C14231|1138.592025|              1.0|   327.99|          248.99|                  79.0|         0.0|           0.333333|                      0.25|                        0.166667|                   0.0|               0|            9|      1200.0| 559.386373|      902.962826|             0.0|    12|                             4.0|               0.0|                                   6.0|               2.0|               10.0|       4115.0|                   0.0|                8288.0|        679.0|                   0.0|                        54.0|         3385.0|                    0.0|                      7.0|        6528.0|                1794.0|                         0.0|         0.0|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+--------------------------------+------------------+--------------------------------------+------------------+-------------------+-------------+----------------------+----------------------+-------------+----------------------+----------------------------+---------------+-----------------------+-------------------------+--------------+----------------------+----------------------------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_r.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oO3OAc-nXf9l",
        "outputId": "8675d66f-67d5-496e-b0e1-488dd0128784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CUST_ID',\n",
              " 'BALANCE',\n",
              " 'BALANCE_FREQUENCY',\n",
              " 'PURCHASES',\n",
              " 'ONEOFF_PURCHASES',\n",
              " 'INSTALLMENTS_PURCHASES',\n",
              " 'CASH_ADVANCE',\n",
              " 'PURCHASES_FREQUENCY',\n",
              " 'ONEOFF_PURCHASES_FREQUENCY',\n",
              " 'PURCHASES_INSTALLMENTS_FREQUENCY',\n",
              " 'CASH_ADVANCE_FREQUENCY',\n",
              " 'CASH_ADVANCE_TRX',\n",
              " 'PURCHASES_TRX',\n",
              " 'CREDIT_LIMIT',\n",
              " 'PAYMENTS',\n",
              " 'MINIMUM_PAYMENTS',\n",
              " 'PRC_FULL_PAYMENT',\n",
              " 'TENURE',\n",
              " 'ONEOFF_PURCHASES_FREQUENCY_index',\n",
              " 'CASH_ADVANCE_index',\n",
              " 'PURCHASES_INSTALLMENTS_FREQUENCY_index',\n",
              " 'CREDIT_LIMIT_index',\n",
              " 'PURCHASES_TRX_index',\n",
              " 'CUST_ID_index',\n",
              " 'CASH_ADVANCE_TRX_index',\n",
              " 'MINIMUM_PAYMENTS_index',\n",
              " 'BALANCE_index',\n",
              " 'PRC_FULL_PAYMENT_index',\n",
              " 'INSTALLMENTS_PURCHASES_index',\n",
              " 'PURCHASES_index',\n",
              " 'BALANCE_FREQUENCY_index',\n",
              " 'PURCHASES_FREQUENCY_index',\n",
              " 'PAYMENTS_index',\n",
              " 'ONEOFF_PURCHASES_index',\n",
              " 'CASH_ADVANCE_FREQUENCY_index',\n",
              " 'TENURE_index']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print((df_r.count(), len(df_r.columns))) #to get shape of a pyspark dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bV1ZSaaNX7pe",
        "outputId": "a36d0581-6f77-477b-ea44-33644093bd0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8950, 36)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_r.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEONSlZeXsbq",
        "outputId": "b99f3a81-9cb1-4cad-8217-66aa9090eb91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CUST_ID: string (nullable = true)\n",
            " |-- BALANCE: double (nullable = true)\n",
            " |-- BALANCE_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES: double (nullable = true)\n",
            " |-- INSTALLMENTS_PURCHASES: double (nullable = true)\n",
            " |-- CASH_ADVANCE: double (nullable = true)\n",
            " |-- PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_TRX: long (nullable = true)\n",
            " |-- PURCHASES_TRX: long (nullable = true)\n",
            " |-- CREDIT_LIMIT: double (nullable = true)\n",
            " |-- PAYMENTS: double (nullable = true)\n",
            " |-- MINIMUM_PAYMENTS: double (nullable = true)\n",
            " |-- PRC_FULL_PAYMENT: double (nullable = true)\n",
            " |-- TENURE: long (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY_index: double (nullable = false)\n",
            " |-- CASH_ADVANCE_index: double (nullable = false)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY_index: double (nullable = false)\n",
            " |-- CREDIT_LIMIT_index: double (nullable = false)\n",
            " |-- PURCHASES_TRX_index: double (nullable = false)\n",
            " |-- CUST_ID_index: double (nullable = false)\n",
            " |-- CASH_ADVANCE_TRX_index: double (nullable = false)\n",
            " |-- MINIMUM_PAYMENTS_index: double (nullable = false)\n",
            " |-- BALANCE_index: double (nullable = false)\n",
            " |-- PRC_FULL_PAYMENT_index: double (nullable = false)\n",
            " |-- INSTALLMENTS_PURCHASES_index: double (nullable = false)\n",
            " |-- PURCHASES_index: double (nullable = false)\n",
            " |-- BALANCE_FREQUENCY_index: double (nullable = false)\n",
            " |-- PURCHASES_FREQUENCY_index: double (nullable = false)\n",
            " |-- PAYMENTS_index: double (nullable = false)\n",
            " |-- ONEOFF_PURCHASES_index: double (nullable = false)\n",
            " |-- CASH_ADVANCE_FREQUENCY_index: double (nullable = false)\n",
            " |-- TENURE_index: double (nullable = false)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preparation for Linear Regression Model**"
      ],
      "metadata": {
        "id": "f4DBDuC9ODoj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Input all the features in one vector column\n",
        "\n",
        "#using df_r dataframe\n",
        "\n",
        "assembler = VectorAssembler(inputCols=['CUST_ID_index', 'BALANCE_index', 'PURCHASES_index', \n",
        "    'ONEOFF_PURCHASES_index', 'CREDIT_LIMIT_index', 'TENURE_index'], outputCol = 'Attributes')\n",
        "\n",
        "result = assembler.transform(df_r) \n",
        "# same as  - train_dataset = assembler.transform(dataset)\n",
        "\n",
        "#Input vs Output\n",
        "final_data = result.select(\"Attributes\",\"PAYMENTS\")\n",
        "\n",
        "final_data.show()"
      ],
      "metadata": {
        "id": "xKv12K5Opbq-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c33bfb51-5e82-49c9-a989-aaf1e5ba7eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----------+\n",
            "|          Attributes|   PAYMENTS|\n",
            "+--------------------+-----------+\n",
            "|[4096.0,293.0,111...|1461.200691|\n",
            "|[4097.0,3732.0,0....|5695.396335|\n",
            "|[4098.0,4459.0,0....|2781.869782|\n",
            "|[4099.0,8133.0,46...| 267.707871|\n",
            "|[4100.0,5930.0,0....|6801.553821|\n",
            "|[4101.0,4223.0,0....| 446.776969|\n",
            "|[4102.0,170.0,390...|2190.466875|\n",
            "|[4103.0,8650.0,0....|2031.387858|\n",
            "|[4104.0,1637.0,40...| 950.209773|\n",
            "|[4105.0,761.0,372...|3312.651656|\n",
            "|[4106.0,7211.0,19...| 610.491342|\n",
            "|[4107.0,5993.0,13...|  39.556692|\n",
            "|[4108.0,8737.0,27...| 623.314494|\n",
            "|[4109.0,3019.0,20...|1934.825977|\n",
            "|[4110.0,2199.0,50...| 584.660133|\n",
            "|[4111.0,2641.0,44...| 791.115227|\n",
            "|[4112.0,7399.0,47...|2314.208069|\n",
            "|[4113.0,1117.0,0....| 209.297618|\n",
            "|[4114.0,4217.0,17...| 630.766685|\n",
            "|[4115.0,679.0,338...| 559.386373|\n",
            "+--------------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Attributes` are the **input features** (X) from all the columns and `PAYMENTS` is the target column. \n",
        "\n",
        "Let's split the **training** and **testing data** according to our dataset (0.75 and 0.25)"
      ],
      "metadata": {
        "id": "tTHLQK8BZ0LS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Training & Dataset Split**"
      ],
      "metadata": {
        "id": "4bt59ghsOLCv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Split training and testing data\n",
        "train_data,test_data = final_data.randomSplit([0.75,0.25])\n",
        "\n",
        "\n",
        "regressor = LinearRegression(featuresCol = 'Attributes', labelCol = 'PAYMENTS')\n",
        "\n",
        "#Learn to fit the model from training set\n",
        "regressor_model = regressor.fit(train_data)\n",
        "\n",
        "#To predict the prices on testing set\n",
        "reg_pred = regressor_model.evaluate(test_data)\n",
        "\n",
        "#Predict the model\n",
        "reg_pred.predictions.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCuyTWynZ8sb",
        "outputId": "c8487dab-3c93-476a-bfd8-a4803c3bac1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-----------+------------------+\n",
            "|          Attributes|   PAYMENTS|        prediction|\n",
            "+--------------------+-----------+------------------+\n",
            "|(6,[0,1],[4191.0,...| 1282.18451| 1548.734138244636|\n",
            "|(6,[0,1],[4377.0,...| 540.703321| 1178.824432439429|\n",
            "|(6,[0,1],[4571.0,...| 810.389383|1363.6499219758477|\n",
            "|(6,[0,1],[4953.0,...| 180.865117| 1549.400150463565|\n",
            "|(6,[0,1],[5045.0,...|1809.765794| 1275.999536260399|\n",
            "|(6,[0,1],[5180.0,...|        0.0| 1196.911614154033|\n",
            "|(6,[0,1],[5184.0,...| 636.224436|1190.6255869087408|\n",
            "|(6,[0,1],[5323.0,...| 442.578729|1146.4927447974444|\n",
            "|(6,[0,1],[5594.0,...|3585.134646|1259.3788010661892|\n",
            "|(6,[0,1],[5628.0,...|        0.0|1340.2231932896136|\n",
            "|(6,[0,1],[5684.0,...|3489.163639| 1039.559268260854|\n",
            "|(6,[0,1],[5933.0,...| 2644.30567|1015.4386878395787|\n",
            "|(6,[0,1],[6674.0,...| 547.687792| 951.1829159281558|\n",
            "|(6,[0,1],[7056.0,...| 859.631936| 908.7839830783461|\n",
            "|(6,[0,1],[7105.0,...| 592.958317|1004.7636824705899|\n",
            "|(6,[0,1],[7108.0,...| 276.975673|1172.6777247770874|\n",
            "|(6,[0,1],[7297.0,...| 341.938368| 772.0922538001406|\n",
            "|(6,[0,1],[8041.0,...| 949.675235| 903.4382159105758|\n",
            "|(6,[0,1],[8409.0,...|  357.29622| 958.2618345830479|\n",
            "|(6,[0,1],[8464.0,...| 682.485692|  1090.60367555968|\n",
            "+--------------------+-----------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor_model.summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpyU0WwkcX-z",
        "outputId": "c8fc7532-71a8-4828-c215-989e3842b443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyspark.ml.regression.LinearRegressionTrainingSummary at 0x7f4f86acb8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "Y3vYyp5dwOm_"
      },
      "cell_type": "markdown",
      "source": [
        "We can also print the **coefficient** and **intercept** of the regression model by using the following command:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#coefficient of the regression model\n",
        "coeff = regressor_model.coefficients\n",
        "\n",
        "#X and Y intercept\n",
        "intr = regressor_model.intercept\n",
        "\n",
        "print (\"The coefficient of the model is : %a\" %coeff)\n",
        "print (\"The Intercept of the model is : %f\" %intr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7gKGtqYcqbp",
        "outputId": "fbd5979c-a2a6-4831-827d-72b7901d5715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The coefficient of the model is : DenseVector([-0.1355, 0.0645, -0.032, 0.2709, 21.0484, -149.6501])\n",
            "The Intercept of the model is : 1706.034015\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "bYORz3Q9wTSW"
      },
      "cell_type": "markdown",
      "source": [
        "### **Model Evaluation**\n",
        "\n",
        "Once we are done with the basic linear regression operation, we can go a bit further and analyze our model statistically by importing RegressionEvaluator module from Pyspark."
      ]
    },
    {
      "metadata": {
        "id": "8qrQdEj62ptt",
        "outputId": "12f17cff-b3f7-4fac-fe5f-f60be4f6ffee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "eval = RegressionEvaluator(labelCol=\"PAYMENTS\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
        "\n",
        "# Root Mean Square Error\n",
        "rmse = eval.evaluate(reg_pred.predictions)\n",
        "print(\"RMSE: %.3f\" % rmse)\n",
        "\n",
        "# Mean Square Error\n",
        "mse = eval.evaluate(reg_pred.predictions, {eval.metricName: \"mse\"})\n",
        "print(\"MSE: %.3f\" % mse)\n",
        "\n",
        "# Mean Absolute Error\n",
        "mae = eval.evaluate(reg_pred.predictions, {eval.metricName: \"mae\"})\n",
        "print(\"MAE: %.3f\" % mae)\n",
        "\n",
        "# r2 - coefficient of determination\n",
        "r2 = eval.evaluate(reg_pred.predictions, {eval.metricName: \"r2\"})\n",
        "print(\"r2: %.3f\" %r2)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 2199.891\n",
            "MSE: 4839520.551\n",
            "MAE: 1363.926\n",
            "r2: 0.077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Classification Model with Pyspark**\n",
        "\n",
        "Data source: df_r"
      ],
      "metadata": {
        "id": "pmoaet6EgKHg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Transformation**\n",
        "\n",
        "Getting our label data & target column"
      ],
      "metadata": {
        "id": "fOQhVojdUZeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# we use a fucntion to creat a new column called target\n",
        "#https://towardsdatascience.com/5-ways-to-add-a-new-column-in-a-pyspark-dataframe-4e75c2fd8c08"
      ],
      "metadata": {
        "id": "eW3k2lAHWVNN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import functions as f\n",
        "xy = data_test.withColumn('label', f.when(f.col('PURCHASES_FREQUENCY') > 0.5, \"Yes\").otherwise(\"No\"))\n",
        "# I can also say \"True of False\""
      ],
      "metadata": {
        "id": "v0nupAO5L8YH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xy.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDRAS_QPS4j-",
        "outputId": "18dfadd7-28a6-4503-aa63-49400331431b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|label|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+\n",
            "| C14212| 105.069311|              1.0|  1344.87|         1344.87|                   0.0|         0.0|           0.916667|                  0.916667|                             0.0|                   0.0|               0|           12|      1700.0|1461.200691|      176.308343|            0.25|    12|  Yes|\n",
            "| C14213|2446.709596|              1.0|      0.0|             0.0|                   0.0| 5281.049743|                0.0|                       0.0|                             0.0|                  0.25|               6|            0|      5000.0|5695.396335|      696.156899|             0.0|    12|   No|\n",
            "| C14214| 295.374207|         0.909091|      0.0|             0.0|                   0.0| 2032.983385|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      1200.0|2781.869782|      174.631958|             0.0|    12|   No|\n",
            "| C14215| 822.755885|              1.0|   531.14|          359.24|                 171.9|         0.0|                1.0|                  0.428571|                        0.571429|                   0.0|               0|           10|      3000.0| 267.707871|      117.131173|             0.0|     7|  Yes|\n",
            "| C14216|4433.582508|              1.0|      0.0|             0.0|                   0.0| 1434.153185|                0.0|                       0.0|                             0.0|              0.416667|               9|            0|      5500.0|6801.553821|     1209.064809|        0.083333|    12|   No|\n",
            "| C14217|2785.584338|              1.0|      0.0|             0.0|                   0.0|  144.330518|                0.0|                       0.0|                             0.0|                   0.1|               1|            0|      3000.0| 446.776969|       733.62973|             0.0|    10|   No|\n",
            "| C14218|10116.70899|              1.0| 10166.69|          3300.0|               6866.69|         0.0|           0.833333|                  0.333333|                        0.666667|                   0.0|               0|           45|     14000.0|2190.466875|     4253.469898|             0.0|    12|  Yes|\n",
            "| C14219|9440.796576|              1.0|      0.0|             0.0|                   0.0|    5831.378|                0.0|                       0.0|                             0.0|              0.666667|              13|            0|     13000.0|2031.387858|     2432.132089|             0.0|    12|   No|\n",
            "| C14220|1435.975897|              1.0|   418.56|          418.56|                   0.0|         0.0|           0.333333|                  0.333333|                             0.0|                   0.0|               0|            4|      2150.0| 950.209773|      466.163755|             0.0|    12|   No|\n",
            "| C14221|1160.514847|              1.0|  3726.79|           664.0|               3062.79|         0.0|                1.0|                  0.083333|                             1.0|                   0.0|               0|           89|      3000.0|3312.651656|      310.179028|        0.083333|    12|  Yes|\n",
            "| C14222| 634.759973|              1.0|    19.91|             0.0|                 19.91|         0.0|           0.083333|                       0.0|                        0.083333|                   0.0|               0|            1|      6000.0| 610.491342|      246.804668|             0.0|    12|   No|\n",
            "| C14223|  45.785125|         0.636364|     12.0|             0.0|                  12.0|  166.160258|                1.0|                       0.0|                        0.916667|              0.083333|               3|           12|      1200.0|  39.556692|       46.550149|             0.0|    12|  Yes|\n",
            "| C14224| 968.504914|              1.0|   257.71|           45.65|                212.06|         0.0|           0.166667|                  0.083333|                        0.166667|                   0.0|               0|            4|      1000.0| 623.314494|      842.242726|             0.0|    12|   No|\n",
            "| C14225| 200.806352|              1.0|  1957.45|         1799.14|                158.31|         0.0|                1.0|                       1.0|                        0.166667|                   0.0|               0|           49|      6000.0|1934.825977|      193.934077|            0.75|    12|  Yes|\n",
            "| C14226|1634.559235|              1.0|  1062.94|         1006.18|                 56.76|         0.0|           0.583333|                       0.5|                        0.166667|                   0.0|               0|           15|      1800.0| 584.660133|     1332.692469|             0.0|    12|  Yes|\n",
            "| C14227|1839.926709|              1.0|   487.75|             0.0|                487.75|  266.614527|                0.5|                       0.0|                        0.416667|              0.083333|               1|            9|      3500.0| 791.115227|      385.482464|             0.0|    12|   No|\n",
            "| C14228|6743.313868|              1.0|  5591.26|          1770.5|               3820.76| 1074.347669|                1.0|                       1.0|                             1.0|                  0.25|               4|          162|      9500.0|2314.208069|     1682.939354|             0.0|    12|  Yes|\n",
            "| C14229|1267.054186|              1.0|      0.0|             0.0|                   0.0| 1253.614385|                0.0|                       0.0|                             0.0|              0.166667|               8|            0|      1500.0| 209.297618|      470.721288|             0.0|    12|   No|\n",
            "| C14230|2778.737121|              1.0|   1973.0|          1973.0|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            2|      6500.0| 630.766685|      728.216731|             0.0|    12|   No|\n",
            "| C14231|1138.592025|              1.0|   327.99|          248.99|                  79.0|         0.0|           0.333333|                      0.25|                        0.166667|                   0.0|               0|            9|      1200.0| 559.386373|      902.962826|             0.0|    12|   No|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xy.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8z4RrFVJMd-N",
        "outputId": "3f333006-f077-4922-b31b-59304a3522b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CUST_ID',\n",
              " 'BALANCE',\n",
              " 'BALANCE_FREQUENCY',\n",
              " 'PURCHASES',\n",
              " 'ONEOFF_PURCHASES',\n",
              " 'INSTALLMENTS_PURCHASES',\n",
              " 'CASH_ADVANCE',\n",
              " 'PURCHASES_FREQUENCY',\n",
              " 'ONEOFF_PURCHASES_FREQUENCY',\n",
              " 'PURCHASES_INSTALLMENTS_FREQUENCY',\n",
              " 'CASH_ADVANCE_FREQUENCY',\n",
              " 'CASH_ADVANCE_TRX',\n",
              " 'PURCHASES_TRX',\n",
              " 'CREDIT_LIMIT',\n",
              " 'PAYMENTS',\n",
              " 'MINIMUM_PAYMENTS',\n",
              " 'PRC_FULL_PAYMENT',\n",
              " 'TENURE',\n",
              " 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xy.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujwHw7FnThZU",
        "outputId": "7ec75066-3133-4026-bb84-e0726a8b94ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CUST_ID: string (nullable = true)\n",
            " |-- BALANCE: double (nullable = true)\n",
            " |-- BALANCE_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES: double (nullable = true)\n",
            " |-- INSTALLMENTS_PURCHASES: double (nullable = true)\n",
            " |-- CASH_ADVANCE: double (nullable = true)\n",
            " |-- PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_TRX: long (nullable = true)\n",
            " |-- PURCHASES_TRX: long (nullable = true)\n",
            " |-- CREDIT_LIMIT: double (nullable = true)\n",
            " |-- PAYMENTS: double (nullable = true)\n",
            " |-- MINIMUM_PAYMENTS: double (nullable = true)\n",
            " |-- PRC_FULL_PAYMENT: double (nullable = true)\n",
            " |-- TENURE: long (nullable = true)\n",
            " |-- label: string (nullable = false)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(xy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPsjcesx7upt",
        "outputId": "3f4099f7-7652-46b8-8ad7-89c671071659"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pyspark.sql.dataframe.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = xy.withColumn(\"label\", xy.PURCHASES_FREQUENCY>=0.5)\n",
        "train = xy.withColumn(\"label\", xy[\"label\"].cast(\"string\"))\n",
        "\n",
        "from pyspark.ml.feature import StringIndexer\n",
        "indexer2 = StringIndexer(inputCol=\"label\", outputCol=\"target\")\n",
        "training_cl = indexer2.fit(train).transform(train)\n",
        "\n",
        "training_cl.limit(5).toPandas()\n",
        "#target column created"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "euII9Vi9FX62",
        "outputId": "c4460e45-3f46-4242-edb8-cbc8f76d9abc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4169b1eb-8e4b-4750-9af4-617be2bf58b3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CUST_ID</th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "      <th>label</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C14212</td>\n",
              "      <td>105.069311</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1344.87</td>\n",
              "      <td>1344.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>1461.200691</td>\n",
              "      <td>176.308343</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>12</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C14213</td>\n",
              "      <td>2446.709596</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5281.049743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5695.396335</td>\n",
              "      <td>696.156899</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C14214</td>\n",
              "      <td>295.374207</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2032.983385</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2781.869782</td>\n",
              "      <td>174.631958</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C14215</td>\n",
              "      <td>822.755885</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>531.14</td>\n",
              "      <td>359.24</td>\n",
              "      <td>171.9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>267.707871</td>\n",
              "      <td>117.131173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C14216</td>\n",
              "      <td>4433.582508</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1434.153185</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>5500.0</td>\n",
              "      <td>6801.553821</td>\n",
              "      <td>1209.064809</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>12</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4169b1eb-8e4b-4750-9af4-617be2bf58b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4169b1eb-8e4b-4750-9af4-617be2bf58b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "  CUST_ID      BALANCE  BALANCE_FREQUENCY  ...  TENURE  label  target\n",
              "0  C14212   105.069311           1.000000  ...      12    Yes     1.0\n",
              "1  C14213  2446.709596           1.000000  ...      12     No     0.0\n",
              "2  C14214   295.374207           0.909091  ...      12     No     0.0\n",
              "3  C14215   822.755885           1.000000  ...       7    Yes     1.0\n",
              "4  C14216  4433.582508           1.000000  ...      12     No     0.0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_cl.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTQMcOUoXh6v",
        "outputId": "501b92cb-7552-4886-f506-b3377a3db1a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+------+\n",
            "|CUST_ID|    BALANCE|BALANCE_FREQUENCY|PURCHASES|ONEOFF_PURCHASES|INSTALLMENTS_PURCHASES|CASH_ADVANCE|PURCHASES_FREQUENCY|ONEOFF_PURCHASES_FREQUENCY|PURCHASES_INSTALLMENTS_FREQUENCY|CASH_ADVANCE_FREQUENCY|CASH_ADVANCE_TRX|PURCHASES_TRX|CREDIT_LIMIT|   PAYMENTS|MINIMUM_PAYMENTS|PRC_FULL_PAYMENT|TENURE|label|target|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+------+\n",
            "| C14212| 105.069311|              1.0|  1344.87|         1344.87|                   0.0|         0.0|           0.916667|                  0.916667|                             0.0|                   0.0|               0|           12|      1700.0|1461.200691|      176.308343|            0.25|    12|  Yes|   1.0|\n",
            "| C14213|2446.709596|              1.0|      0.0|             0.0|                   0.0| 5281.049743|                0.0|                       0.0|                             0.0|                  0.25|               6|            0|      5000.0|5695.396335|      696.156899|             0.0|    12|   No|   0.0|\n",
            "| C14214| 295.374207|         0.909091|      0.0|             0.0|                   0.0| 2032.983385|                0.0|                       0.0|                             0.0|                  0.25|               4|            0|      1200.0|2781.869782|      174.631958|             0.0|    12|   No|   0.0|\n",
            "| C14215| 822.755885|              1.0|   531.14|          359.24|                 171.9|         0.0|                1.0|                  0.428571|                        0.571429|                   0.0|               0|           10|      3000.0| 267.707871|      117.131173|             0.0|     7|  Yes|   1.0|\n",
            "| C14216|4433.582508|              1.0|      0.0|             0.0|                   0.0| 1434.153185|                0.0|                       0.0|                             0.0|              0.416667|               9|            0|      5500.0|6801.553821|     1209.064809|        0.083333|    12|   No|   0.0|\n",
            "| C14217|2785.584338|              1.0|      0.0|             0.0|                   0.0|  144.330518|                0.0|                       0.0|                             0.0|                   0.1|               1|            0|      3000.0| 446.776969|       733.62973|             0.0|    10|   No|   0.0|\n",
            "| C14218|10116.70899|              1.0| 10166.69|          3300.0|               6866.69|         0.0|           0.833333|                  0.333333|                        0.666667|                   0.0|               0|           45|     14000.0|2190.466875|     4253.469898|             0.0|    12|  Yes|   1.0|\n",
            "| C14219|9440.796576|              1.0|      0.0|             0.0|                   0.0|    5831.378|                0.0|                       0.0|                             0.0|              0.666667|              13|            0|     13000.0|2031.387858|     2432.132089|             0.0|    12|   No|   0.0|\n",
            "| C14220|1435.975897|              1.0|   418.56|          418.56|                   0.0|         0.0|           0.333333|                  0.333333|                             0.0|                   0.0|               0|            4|      2150.0| 950.209773|      466.163755|             0.0|    12|   No|   0.0|\n",
            "| C14221|1160.514847|              1.0|  3726.79|           664.0|               3062.79|         0.0|                1.0|                  0.083333|                             1.0|                   0.0|               0|           89|      3000.0|3312.651656|      310.179028|        0.083333|    12|  Yes|   1.0|\n",
            "| C14222| 634.759973|              1.0|    19.91|             0.0|                 19.91|         0.0|           0.083333|                       0.0|                        0.083333|                   0.0|               0|            1|      6000.0| 610.491342|      246.804668|             0.0|    12|   No|   0.0|\n",
            "| C14223|  45.785125|         0.636364|     12.0|             0.0|                  12.0|  166.160258|                1.0|                       0.0|                        0.916667|              0.083333|               3|           12|      1200.0|  39.556692|       46.550149|             0.0|    12|  Yes|   1.0|\n",
            "| C14224| 968.504914|              1.0|   257.71|           45.65|                212.06|         0.0|           0.166667|                  0.083333|                        0.166667|                   0.0|               0|            4|      1000.0| 623.314494|      842.242726|             0.0|    12|   No|   0.0|\n",
            "| C14225| 200.806352|              1.0|  1957.45|         1799.14|                158.31|         0.0|                1.0|                       1.0|                        0.166667|                   0.0|               0|           49|      6000.0|1934.825977|      193.934077|            0.75|    12|  Yes|   1.0|\n",
            "| C14226|1634.559235|              1.0|  1062.94|         1006.18|                 56.76|         0.0|           0.583333|                       0.5|                        0.166667|                   0.0|               0|           15|      1800.0| 584.660133|     1332.692469|             0.0|    12|  Yes|   1.0|\n",
            "| C14227|1839.926709|              1.0|   487.75|             0.0|                487.75|  266.614527|                0.5|                       0.0|                        0.416667|              0.083333|               1|            9|      3500.0| 791.115227|      385.482464|             0.0|    12|   No|   0.0|\n",
            "| C14228|6743.313868|              1.0|  5591.26|          1770.5|               3820.76| 1074.347669|                1.0|                       1.0|                             1.0|                  0.25|               4|          162|      9500.0|2314.208069|     1682.939354|             0.0|    12|  Yes|   1.0|\n",
            "| C14229|1267.054186|              1.0|      0.0|             0.0|                   0.0| 1253.614385|                0.0|                       0.0|                             0.0|              0.166667|               8|            0|      1500.0| 209.297618|      470.721288|             0.0|    12|   No|   0.0|\n",
            "| C14230|2778.737121|              1.0|   1973.0|          1973.0|                   0.0|         0.0|           0.166667|                  0.166667|                             0.0|                   0.0|               0|            2|      6500.0| 630.766685|      728.216731|             0.0|    12|   No|   0.0|\n",
            "| C14231|1138.592025|              1.0|   327.99|          248.99|                  79.0|         0.0|           0.333333|                      0.25|                        0.166667|                   0.0|               0|            9|      1200.0| 559.386373|      902.962826|             0.0|    12|   No|   0.0|\n",
            "+-------+-----------+-----------------+---------+----------------+----------------------+------------+-------------------+--------------------------+--------------------------------+----------------------+----------------+-------------+------------+-----------+----------------+----------------+------+-----+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_cl.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ln23ZEgXm30",
        "outputId": "554c07d1-a275-45ad-dbfa-048ea482e858"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CUST_ID: string (nullable = true)\n",
            " |-- BALANCE: double (nullable = true)\n",
            " |-- BALANCE_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES: double (nullable = true)\n",
            " |-- INSTALLMENTS_PURCHASES: double (nullable = true)\n",
            " |-- CASH_ADVANCE: double (nullable = true)\n",
            " |-- PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- ONEOFF_PURCHASES_FREQUENCY: double (nullable = true)\n",
            " |-- PURCHASES_INSTALLMENTS_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_FREQUENCY: double (nullable = true)\n",
            " |-- CASH_ADVANCE_TRX: long (nullable = true)\n",
            " |-- PURCHASES_TRX: long (nullable = true)\n",
            " |-- CREDIT_LIMIT: double (nullable = true)\n",
            " |-- PAYMENTS: double (nullable = true)\n",
            " |-- MINIMUM_PAYMENTS: double (nullable = true)\n",
            " |-- PRC_FULL_PAYMENT: double (nullable = true)\n",
            " |-- TENURE: long (nullable = true)\n",
            " |-- label: string (nullable = false)\n",
            " |-- target: double (nullable = false)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Properties**\n",
        "\n"
      ],
      "metadata": {
        "id": "CpH2f-hcCJZc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate statistical varables for columns \n",
        "training_cl.describe().toPandas().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "d7a6xBVfKNCf",
        "outputId": "d93b46f3-5d05-4175-c89e-dcca9aec4724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-aac70e51-41da-4b60-a098-e696779edb88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>summary</th>\n",
              "      <td>count</td>\n",
              "      <td>mean</td>\n",
              "      <td>stddev</td>\n",
              "      <td>min</td>\n",
              "      <td>max</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUST_ID</th>\n",
              "      <td>8950</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>C10001</td>\n",
              "      <td>C19190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BALANCE</th>\n",
              "      <td>8950</td>\n",
              "      <td>1564.4748276781013</td>\n",
              "      <td>2081.5318794565537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19043.13856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.8772707255865909</td>\n",
              "      <td>0.23690400268476833</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PURCHASES</th>\n",
              "      <td>8950</td>\n",
              "      <td>1003.2048335195524</td>\n",
              "      <td>2136.634781872888</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49039.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <td>8950</td>\n",
              "      <td>592.4373709497205</td>\n",
              "      <td>1659.887917437812</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40761.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <td>8950</td>\n",
              "      <td>411.06764469273656</td>\n",
              "      <td>904.3381151753822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <td>8950</td>\n",
              "      <td>978.8711124654759</td>\n",
              "      <td>2097.1638766432325</td>\n",
              "      <td>0.0</td>\n",
              "      <td>47137.21176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.49035054837988934</td>\n",
              "      <td>0.40137074736904077</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.20245768357542027</td>\n",
              "      <td>0.29833606518471945</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.36443734156424507</td>\n",
              "      <td>0.39744777974541307</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.13514420033519575</td>\n",
              "      <td>0.20012138814749697</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <td>8950</td>\n",
              "      <td>3.2488268156424582</td>\n",
              "      <td>6.824646744165834</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <td>8950</td>\n",
              "      <td>14.709832402234637</td>\n",
              "      <td>24.857649109884736</td>\n",
              "      <td>0</td>\n",
              "      <td>358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <td>8950</td>\n",
              "      <td>4493.947277241676</td>\n",
              "      <td>3638.9225426545486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAYMENTS</th>\n",
              "      <td>8950</td>\n",
              "      <td>1733.1438520248068</td>\n",
              "      <td>2895.06375690457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50721.48336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <td>8950</td>\n",
              "      <td>833.9834531719565</td>\n",
              "      <td>2335.989918070599</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76406.20752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.15371464849162056</td>\n",
              "      <td>0.2924991962338786</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TENURE</th>\n",
              "      <td>8950</td>\n",
              "      <td>11.51731843575419</td>\n",
              "      <td>1.3383307693673268</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <td>8950</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>8950</td>\n",
              "      <td>0.4633519553072626</td>\n",
              "      <td>0.49868297225889024</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aac70e51-41da-4b60-a098-e696779edb88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aac70e51-41da-4b60-a098-e696779edb88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aac70e51-41da-4b60-a098-e696779edb88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      0  ...            4\n",
              "summary                           count  ...          max\n",
              "CUST_ID                            8950  ...       C19190\n",
              "BALANCE                            8950  ...  19043.13856\n",
              "BALANCE_FREQUENCY                  8950  ...          1.0\n",
              "PURCHASES                          8950  ...     49039.57\n",
              "ONEOFF_PURCHASES                   8950  ...     40761.25\n",
              "INSTALLMENTS_PURCHASES             8950  ...      22500.0\n",
              "CASH_ADVANCE                       8950  ...  47137.21176\n",
              "PURCHASES_FREQUENCY                8950  ...          1.0\n",
              "ONEOFF_PURCHASES_FREQUENCY         8950  ...          1.0\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY   8950  ...          1.0\n",
              "CASH_ADVANCE_FREQUENCY             8950  ...          1.5\n",
              "CASH_ADVANCE_TRX                   8950  ...          123\n",
              "PURCHASES_TRX                      8950  ...          358\n",
              "CREDIT_LIMIT                       8950  ...      30000.0\n",
              "PAYMENTS                           8950  ...  50721.48336\n",
              "MINIMUM_PAYMENTS                   8950  ...  76406.20752\n",
              "PRC_FULL_PAYMENT                   8950  ...          1.0\n",
              "TENURE                             8950  ...           12\n",
              "label                              8950  ...          Yes\n",
              "target                             8950  ...          1.0\n",
              "\n",
              "[21 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot scatter matrix between the columns \n",
        "import pandas as pd\n",
        "from pandas.plotting import scatter_matrix\n",
        "numeric_features = [t[0] for t in training_cl.dtypes if t[1] == 'int' or t[1] == 'double']\n",
        "\n",
        "sampled_data = training_cl.select(numeric_features).sample(False, 0.8).toPandas()\n",
        "axs = scatter_matrix(sampled_data, figsize=(8, 15))\n",
        "n = len(sampled_data.columns)\n",
        "for i in range(n):\n",
        "    v = axs[i, 0]\n",
        "    v.yaxis.label.set_rotation(0)\n",
        "    v.yaxis.label.set_ha('right')\n",
        "    v.set_yticks(())\n",
        "    h = axs[n-1, i]\n",
        "    h.xaxis.label.set_rotation(90)\n",
        "    h.set_xticks(())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VooctpSxL0WF",
        "outputId": "32e5855f-2a6b-4bfb-bfa2-e71eda10ed28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x1080 with 225 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix between the input features and Class\n",
        "import six\n",
        "for i in training_cl.columns:\n",
        "    if not( isinstance(training_cl.select(i).take(1)[0][0], six.string_types)):\n",
        "        print( \"Correlation to Class for \", i, training_cl.stat.corr('target',i))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lyNSnBGMlCL",
        "outputId": "67bd3fc8-ab23-4804-b357-5a85167afe43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation to Class for  BALANCE -0.057859712421735245\n",
            "Correlation to Class for  BALANCE_FREQUENCY 0.2431212549738119\n",
            "Correlation to Class for  PURCHASES 0.3538977873678623\n",
            "Correlation to Class for  ONEOFF_PURCHASES 0.23656837443714865\n",
            "Correlation to Class for  INSTALLMENTS_PURCHASES 0.4020578361665616\n",
            "Correlation to Class for  CASH_ADVANCE -0.17088024750624253\n",
            "Correlation to Class for  PURCHASES_FREQUENCY 0.9178944269853585\n",
            "Correlation to Class for  ONEOFF_PURCHASES_FREQUENCY 0.4490101142301152\n",
            "Correlation to Class for  PURCHASES_INSTALLMENTS_FREQUENCY 0.798918146252267\n",
            "Correlation to Class for  CASH_ADVANCE_FREQUENCY -0.24354911963745834\n",
            "Correlation to Class for  CASH_ADVANCE_TRX -0.155168623483919\n",
            "Correlation to Class for  PURCHASES_TRX 0.5176416770591831\n",
            "Correlation to Class for  CREDIT_LIMIT 0.10493081759365298\n",
            "Correlation to Class for  PAYMENTS 0.10065122200909772\n",
            "Correlation to Class for  MINIMUM_PAYMENTS 0.012805165805485921\n",
            "Correlation to Class for  PRC_FULL_PAYMENT 0.27544566793390013\n",
            "Correlation to Class for  TENURE 0.03912549673062846\n",
            "Correlation to Class for  target 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This would guide my decision towards feature selection for my classification model."
      ],
      "metadata": {
        "id": "OesPZLNWYjjW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Prep Data for Machine Learning with Pyspar**k\n",
        "\n",
        "Prepare Data for Machine Learning algorithm. Divide data into only two columns since spark support two columns only, one for features and the second one is label [Class/Y]"
      ],
      "metadata": {
        "id": "gmH0qHPDO5od"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Spark support only two columns during training one for features and second one is labels\n",
        "# Vector Assembler is used to assemble all the features used for training \n",
        "# Y (Class) in the label where to train a Classification model for estimating the \n",
        "# default payment\n",
        "\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "vectorAssembler = VectorAssembler(inputCols = ['PURCHASES_FREQUENCY', 'BALANCE', 'PURCHASES_INSTALLMENTS_FREQUENCY', \n",
        "    'PRC_FULL_PAYMENT', 'PURCHASES', 'CASH_ADVANCE_FREQUENCY'], outputCol = 'features')\n",
        "\n",
        "\n",
        "default_df = vectorAssembler.transform(training_cl)\n",
        "\n",
        "default_df= default_df.select(['features', 'target'])\n",
        "default_df.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRQu4YPGOtwn",
        "outputId": "3ef2062a-c0fb-474d-f3c3-9f76d2442580"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+------+\n",
            "|            features|target|\n",
            "+--------------------+------+\n",
            "|[0.916667,105.069...|   1.0|\n",
            "|(6,[1,5],[2446.70...|   0.0|\n",
            "|(6,[1,5],[295.374...|   0.0|\n",
            "|[1.0,822.755885,0...|   1.0|\n",
            "|[0.0,4433.582508,...|   0.0|\n",
            "|(6,[1,5],[2785.58...|   0.0|\n",
            "|[0.833333,10116.7...|   1.0|\n",
            "|(6,[1,5],[9440.79...|   0.0|\n",
            "|[0.333333,1435.97...|   0.0|\n",
            "|[1.0,1160.514847,...|   1.0|\n",
            "|[0.083333,634.759...|   0.0|\n",
            "|[1.0,45.785125,0....|   1.0|\n",
            "|[0.166667,968.504...|   0.0|\n",
            "|[1.0,200.806352,0...|   1.0|\n",
            "|[0.583333,1634.55...|   1.0|\n",
            "|[0.5,1839.926709,...|   0.0|\n",
            "|[1.0,6743.313868,...|   1.0|\n",
            "|(6,[1,5],[1267.05...|   0.0|\n",
            "|[0.166667,2778.73...|   0.0|\n",
            "|[0.333333,1138.59...|   0.0|\n",
            "+--------------------+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting data into training and testing 80% training and 20% testing\n",
        "weights = [.8, .2]\n",
        "#seed = 42 # seed=0L\n",
        "rawTrainData, rawValidationData = default_df.randomSplit(weights)"
      ],
      "metadata": {
        "id": "Kae6lWNzRWnD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rawValidationData .show(100) #test dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGL5ZONwRqmk",
        "outputId": "1b543b8d-622c-4e7d-d07d-9a48cf03bcaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+------+\n",
            "|            features|target|\n",
            "+--------------------+------+\n",
            "|(6,[1,5],[4.81721...|   0.0|\n",
            "|(6,[1,5],[37.3247...|   0.0|\n",
            "|(6,[1,5],[40.7642...|   0.0|\n",
            "|(6,[1,5],[45.1608...|   0.0|\n",
            "|(6,[1,5],[46.9965...|   0.0|\n",
            "|(6,[1,5],[71.8373...|   0.0|\n",
            "|(6,[1,5],[85.9007...|   0.0|\n",
            "|(6,[1,5],[116.172...|   0.0|\n",
            "|(6,[1,5],[123.822...|   0.0|\n",
            "|(6,[1,5],[134.613...|   0.0|\n",
            "|(6,[1,5],[137.507...|   0.0|\n",
            "|(6,[1,5],[150.897...|   0.0|\n",
            "|(6,[1,5],[153.602...|   0.0|\n",
            "|(6,[1,5],[173.075...|   0.0|\n",
            "|(6,[1,5],[174.909...|   0.0|\n",
            "|(6,[1,5],[195.361...|   0.0|\n",
            "|(6,[1,5],[197.159...|   0.0|\n",
            "|(6,[1,5],[211.162...|   0.0|\n",
            "|(6,[1,5],[214.592...|   0.0|\n",
            "|(6,[1,5],[218.890...|   0.0|\n",
            "|(6,[1,5],[229.272...|   0.0|\n",
            "|(6,[1,5],[249.489...|   0.0|\n",
            "|(6,[1,5],[298.651...|   0.0|\n",
            "|(6,[1,5],[300.819...|   0.0|\n",
            "|(6,[1,5],[303.740...|   0.0|\n",
            "|(6,[1,5],[312.197...|   0.0|\n",
            "|(6,[1,5],[323.801...|   0.0|\n",
            "|(6,[1,5],[324.334...|   0.0|\n",
            "|(6,[1,5],[350.306...|   0.0|\n",
            "|(6,[1,5],[376.997...|   0.0|\n",
            "|(6,[1,5],[390.008...|   0.0|\n",
            "|(6,[1,5],[435.490...|   0.0|\n",
            "|(6,[1,5],[443.339...|   0.0|\n",
            "|(6,[1,5],[445.179...|   0.0|\n",
            "|(6,[1,5],[452.907...|   0.0|\n",
            "|(6,[1,5],[460.331...|   0.0|\n",
            "|(6,[1,5],[468.851...|   0.0|\n",
            "|(6,[1,5],[526.858...|   0.0|\n",
            "|(6,[1,5],[539.162...|   0.0|\n",
            "|(6,[1,5],[561.157...|   0.0|\n",
            "|(6,[1,5],[587.625...|   0.0|\n",
            "|(6,[1,5],[617.413...|   0.0|\n",
            "|(6,[1,5],[624.633...|   0.0|\n",
            "|(6,[1,5],[653.881...|   0.0|\n",
            "|(6,[1,5],[662.691...|   0.0|\n",
            "|(6,[1,5],[664.911...|   0.0|\n",
            "|(6,[1,5],[683.391...|   0.0|\n",
            "|(6,[1,5],[694.707...|   0.0|\n",
            "|(6,[1,5],[722.855...|   0.0|\n",
            "|(6,[1,5],[741.053...|   0.0|\n",
            "|(6,[1,5],[804.006...|   0.0|\n",
            "|(6,[1,5],[811.638...|   0.0|\n",
            "|(6,[1,5],[819.462...|   0.0|\n",
            "|(6,[1,5],[825.207...|   0.0|\n",
            "|(6,[1,5],[827.000...|   0.0|\n",
            "|(6,[1,5],[834.789...|   0.0|\n",
            "|(6,[1,5],[844.022...|   0.0|\n",
            "|(6,[1,5],[884.230...|   0.0|\n",
            "|(6,[1,5],[895.308...|   0.0|\n",
            "|(6,[1,5],[912.912...|   0.0|\n",
            "|(6,[1,5],[918.227...|   0.0|\n",
            "|(6,[1,5],[952.260...|   0.0|\n",
            "|(6,[1,5],[954.617...|   0.0|\n",
            "|(6,[1,5],[970.598...|   0.0|\n",
            "|(6,[1,5],[1000.81...|   0.0|\n",
            "|(6,[1,5],[1002.79...|   0.0|\n",
            "|(6,[1,5],[1017.98...|   0.0|\n",
            "|(6,[1,5],[1040.03...|   0.0|\n",
            "|(6,[1,5],[1044.87...|   0.0|\n",
            "|(6,[1,5],[1050.52...|   0.0|\n",
            "|(6,[1,5],[1052.79...|   0.0|\n",
            "|(6,[1,5],[1054.41...|   0.0|\n",
            "|(6,[1,5],[1056.98...|   0.0|\n",
            "|(6,[1,5],[1060.34...|   0.0|\n",
            "|(6,[1,5],[1061.57...|   0.0|\n",
            "|(6,[1,5],[1064.47...|   0.0|\n",
            "|(6,[1,5],[1080.85...|   0.0|\n",
            "|(6,[1,5],[1085.31...|   0.0|\n",
            "|(6,[1,5],[1087.56...|   0.0|\n",
            "|(6,[1,5],[1098.45...|   0.0|\n",
            "|(6,[1,5],[1101.85...|   0.0|\n",
            "|(6,[1,5],[1109.87...|   0.0|\n",
            "|(6,[1,5],[1142.30...|   0.0|\n",
            "|(6,[1,5],[1143.33...|   0.0|\n",
            "|(6,[1,5],[1155.97...|   0.0|\n",
            "|(6,[1,5],[1157.30...|   0.0|\n",
            "|(6,[1,5],[1159.61...|   0.0|\n",
            "|(6,[1,5],[1179.61...|   0.0|\n",
            "|(6,[1,5],[1184.97...|   0.0|\n",
            "|(6,[1,5],[1199.66...|   0.0|\n",
            "|(6,[1,5],[1207.76...|   0.0|\n",
            "|(6,[1,5],[1219.04...|   0.0|\n",
            "|(6,[1,5],[1243.25...|   0.0|\n",
            "|(6,[1,5],[1274.54...|   0.0|\n",
            "|(6,[1,5],[1286.78...|   0.0|\n",
            "|(6,[1,5],[1305.88...|   0.0|\n",
            "|(6,[1,5],[1322.00...|   0.0|\n",
            "|(6,[1,5],[1411.91...|   0.0|\n",
            "|(6,[1,5],[1418.83...|   0.0|\n",
            "|(6,[1,5],[1433.40...|   0.0|\n",
            "+--------------------+------+\n",
            "only showing top 100 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Logistic Regression- Classification**"
      ],
      "metadata": {
        "id": "jlvcU8HISCrV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# training the logistic regression madel\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "lr =  LogisticRegression(featuresCol = 'features', \n",
        "                         labelCol='target', maxIter=10, regParam=0.3, elasticNetParam=0.8)\n",
        "\n",
        "lr_model = lr.fit(rawTrainData)\n",
        "\n",
        "print(\"Coefficients: \" + str(lr_model.coefficients))\n",
        "print(\"Intercept: \" + str(lr_model.intercept))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Mae755YR-Ar",
        "outputId": "2317cba3-1588-4ca4-c89d-b00cdd218227"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients: [1.6735319539883504,0.0,0.20236884315549838,0.0,0.0,0.0]\n",
            "Intercept: -1.0743069568479464\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary of the training data \n",
        "# Extract the summary from the returned LogisticRegressionModel instance trained\n",
        "# in the earlier step\n",
        "\n",
        "trainingSummary = lr_model.summary\n",
        "\n",
        "# Obtain the objective per iteration\n",
        "objectiveHistory = trainingSummary.objectiveHistory\n",
        "print(\"objectiveHistory:\")\n",
        "for objective in objectiveHistory:\n",
        "    print(objective)\n",
        "\n",
        "# Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.\n",
        "trainingSummary.roc.show()\n",
        "print(\"areaUnderROC: \" + str(trainingSummary.areaUnderROC))\n",
        "\n",
        "# Set the model threshold to maximize F-Measure\n",
        "fMeasure = trainingSummary.fMeasureByThreshold\n",
        "maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()\n",
        "bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \\\n",
        "    .select('threshold').head()['threshold']\n",
        "lr.setThreshold(bestThreshold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00lU6jiaSwjK",
        "outputId": "f3d9d766-4717-4b2d-8d5a-2762d03d0863"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "objectiveHistory:\n",
            "0.6909642284923642\n",
            "0.6804386095922033\n",
            "0.6523831617465304\n",
            "0.6463881327943434\n",
            "0.6228766291967845\n",
            "0.6175130865995994\n",
            "0.6157398706208057\n",
            "0.6142618359967075\n",
            "0.6138514489607172\n",
            "0.6125688172646736\n",
            "0.6124232516380379\n",
            "+---+-------------------+\n",
            "|FPR|                TPR|\n",
            "+---+-------------------+\n",
            "|0.0|                0.0|\n",
            "|0.0| 0.3739886125262212|\n",
            "|0.0|0.38267905304165417|\n",
            "|0.0| 0.3958645489961043|\n",
            "|0.0| 0.4294276296074318|\n",
            "|0.0| 0.4396164219358705|\n",
            "|0.0| 0.4408151033862751|\n",
            "|0.0|0.44920587353910696|\n",
            "|0.0|0.44980521426430925|\n",
            "|0.0| 0.4626910398561582|\n",
            "|0.0| 0.4632903805813605|\n",
            "|0.0|0.47228049145939466|\n",
            "|0.0|0.47677554689841173|\n",
            "|0.0| 0.4824692837878334|\n",
            "|0.0| 0.5151333533113575|\n",
            "|0.0| 0.5418040155828588|\n",
            "|0.0| 0.5495954450104885|\n",
            "|0.0| 0.5954450104884627|\n",
            "|0.0| 0.6053341324543002|\n",
            "|0.0| 0.6074318249925083|\n",
            "+---+-------------------+\n",
            "only showing top 20 rows\n",
            "\n",
            "areaUnderROC: 0.9999831637023899\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression_b74ebf22427d"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rawTrainData.describe().show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OqFzSypTOG-",
        "outputId": "916e2fdc-d5c0-499f-f42c-16b283c1352d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-------------------+\n",
            "|summary|             target|\n",
            "+-------+-------------------+\n",
            "|  count|               7146|\n",
            "|   mean|0.46697453120626925|\n",
            "| stddev| 0.4989430380960091|\n",
            "|    min|                0.0|\n",
            "|    max|                1.0|\n",
            "+-------+-------------------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Testing the Logistic Regression (Classification Model)**"
      ],
      "metadata": {
        "id": "ls-wTGTDTQA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the trained logistic regression model\n",
        "\n",
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
        "\n",
        "lr_predictions = lr_model.transform(rawValidationData)\n",
        "lr_predictions.select(\"prediction\",\"target\",\"features\").show(8)\n",
        "\n",
        "# Select (prediction, true label) and compute test error\n",
        "\n",
        "evaluator = MulticlassClassificationEvaluator(\n",
        "    labelCol=\"target\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
        "accuracy = evaluator.evaluate(lr_predictions)\n",
        "\n",
        "print(\"Test Error = %g \" % (1.0 - accuracy))\n",
        "print(\"Accuracy = %g \" % ( accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oJl4mcjTUnh",
        "outputId": "3f5cd4a9-8178-4f1f-c9aa-bcc2ed50ba72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+------+--------------------+\n",
            "|prediction|target|            features|\n",
            "+----------+------+--------------------+\n",
            "|       0.0|   0.0|(6,[1,5],[4.81721...|\n",
            "|       0.0|   0.0|(6,[1,5],[37.3247...|\n",
            "|       0.0|   0.0|(6,[1,5],[40.7642...|\n",
            "|       0.0|   0.0|(6,[1,5],[45.1608...|\n",
            "|       0.0|   0.0|(6,[1,5],[46.9965...|\n",
            "|       0.0|   0.0|(6,[1,5],[71.8373...|\n",
            "|       0.0|   0.0|(6,[1,5],[85.9007...|\n",
            "|       0.0|   0.0|(6,[1,5],[116.172...|\n",
            "+----------+------+--------------------+\n",
            "only showing top 8 rows\n",
            "\n",
            "Test Error = 0.0199557 \n",
            "Accuracy = 0.980044 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "\n",
        "areaUnderROC_Test = BinaryClassificationEvaluator(metricName = 'areaUnderROC', labelCol=\"target\")\n",
        "ART = areaUnderROC_Test.evaluate(lr_predictions)\n",
        "print(\"Area Under Curve test\",ART)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlfJ9K6BTdm9",
        "outputId": "fd233b36-6f34-4893-bff4-2d25cc33f0c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area Under Curve test 0.9998161810368383\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Clustering with Pyspark**\n",
        "\n"
      ],
      "metadata": {
        "id": "EH8-srO8UKiG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Bisecting k-means**\n",
        "\n",
        "\n",
        "Bisecting k-means is a kind of hierarchical clustering using a divisive (or “top-down”) approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.\n",
        "\n",
        "Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.\n",
        "\n",
        "BisectingKMeans is implemented as an Estimator and generates a BisectingKMeansModel as the base model.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "gmE8C20noFJ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.ml.clustering import BisectingKMeans\n",
        "from pyspark.ml.evaluation import ClusteringEvaluator\n",
        "\n",
        "\n",
        "# Trains a bisecting k-means model.\n",
        "bkm = BisectingKMeans().setK(2).setSeed(1)\n",
        "model = bkm.fit(default_df)\n",
        "\n",
        "# Make predictions\n",
        "predictions_cl = model.transform(default_df)\n",
        "\n",
        "\n",
        "# Silhouette score\n",
        "# Evaluate the predictions using the Silhouette score.\n",
        "# Evaluate clustering by computing Silhouette score\n",
        "evaluator = ClusteringEvaluator()\n",
        "\n",
        "silhouette = evaluator.evaluate(predictions_cl)\n",
        "print(\"Silhouette with squared euclidean distance = \" + str(silhouette))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Shows the result.\n",
        "print(\"Cluster Centers: \")\n",
        "centers = model.clusterCenters()\n",
        "for center in centers:\n",
        "    print(center)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeaUx3ADmD8n",
        "outputId": "1e015a87-3f10-4c6d-9783-f29a4b4098dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette with squared euclidean distance = 0.7647965640889247\n",
            "Cluster Centers: \n",
            "[4.82562570e-01 8.85739513e+02 3.57625981e-01 1.72003256e-01\n",
            " 7.22821457e+02 1.09915274e-01]\n",
            "[5.36097446e-01 5.55139318e+03 4.04447545e-01 4.62866390e-02\n",
            " 2.65018799e+03 2.83339922e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction**"
      ],
      "metadata": {
        "id": "BsFIJpRgs3HK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_cl.select(\"features\",\"prediction\").limit(5).toPandas()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7L5E7IrhoMwb",
        "outputId": "597ec1b1-b733-4635-da36-28942e61f78d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                           features  prediction\n",
              "0   [0.916667, 105.069311, 0.0, 0.25, 1344.87, 0.0]           0\n",
              "1           (0.0, 2446.709596, 0.0, 0.0, 0.0, 0.25)           0\n",
              "2            (0.0, 295.374207, 0.0, 0.0, 0.0, 0.25)           0\n",
              "3     [1.0, 822.755885, 0.571429, 0.0, 531.14, 0.0]           0\n",
              "4  [0.0, 4433.582508, 0.0, 0.083333, 0.0, 0.416667]           1"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  **Cluster frequencies**"
      ],
      "metadata": {
        "id": "H1SM4p_FsfLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_cl.groupBy('prediction').count().show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5mOwKdtsb1V",
        "outputId": "ed72744f-0875-4535-b9a4-31bd6163053b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-----+\n",
            "|prediction|count|\n",
            "+----------+-----+\n",
            "|         1| 1302|\n",
            "|         0| 7648|\n",
            "+----------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Note:**\n",
        "\n",
        "**Use toPandas restrictedly:** We want to avoid loading data into memory, calling toPandas() would do this and actually prevent operations from being performed in a distributed mode. Though it could be used say when the data is already aggregated, yet avoid using it for large dataframes."
      ],
      "metadata": {
        "id": "J6D6wzBHl_4Y"
      }
    }
  ]
}