EDA&Classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07RfeEjF8_K1"
      },
      "outputs": [],
      "source": [
        "# installing Pyspark\n",
        "!pip install pyspark"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vWl9BDYVQ8rj"
      },
      "outputs": [],
      "source": [
        "# ingnoring the warning signs\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XlAx0qob-9xR"
      },
      "outputs": [],
      "source": [
        "# importing pyspark functions and libraries\n",
        "from pyspark.sql.functions import sum, col, desc, mean, when\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.appName('Assessment').getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bo_7QfSZ2P-Q"
      },
      "outputs": [],
      "source": [
        "# importing data manipulation and visualisation libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing machine libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "oRf0tk_mOIOl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NEr_L5D4_kkG"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3oMbTZ4Bga7"
      },
      "source": [
        "Loading the facts table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hF97CGMk_Qxt"
      },
      "outputs": [],
      "source": [
        "# reading the datasets\n",
        "df_fact = spark.read.csv('FactInternetSales.csv', header=True, inferSchema=True)\n",
        "df_currency = spark.read.csv('DimCurrency.csv', header=True, inferSchema=True)\n",
        "df_product = spark.read.csv('DimProduct.csv', header=True, inferSchema=True)\n",
        "df_customer = spark.read.csv('DimCustomer.csv', header=True, inferSchema=True)\n",
        "df_Territory = spark.read.csv('DimSalesTerritory.csv', header=True, inferSchema=True)\n",
        "df_Date = spark.read.csv('DimDate.csv', header=True, inferSchema=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RS0RpzCBF0tl"
      },
      "outputs": [],
      "source": [
        "# Creating new columns TotalSpent and Profit\n",
        "df_fact = df_fact.withColumn(\"Profit\", df_fact.UnitPrice - df_fact.TotalProductCost)\n",
        "df_fact = df_fact.withColumn(\"TotalSpent\", df_fact.UnitPrice + df_fact.TaxAmt + df_fact.Freight)\n",
        "# creating a new column to segment the customers in good(1) and bad(0)\n",
        "df_fact = df_fact.withColumn('customerSeg', when(col('TotalSpent') < 537.1, 0).otherwise(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wRkHXsaSB2fF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fef09297-bf40-4e05-d6e1-a74389267039"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+------------+----------+-----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+\n",
            "|ProductKey|OrderDateKey|DueDateKey|ShipDateKey|CustomerKey|PromotionKey|CurrencyKey|SalesTerritoryKey|SalesOrderNumber|SalesOrderLineNumber|RevisionNumber|OrderQuantity|UnitPrice|ExtendedAmount|UnitPriceDiscountPct|DiscountAmount|ProductStandardCost|TotalProductCost|SalesAmount|  TaxAmt|Freight|CarrierTrackingNumber|CustomerPONumber|          OrderDate|            DueDate|           ShipDate|            Profit|        TotalSpent|customerSeg|\n",
            "+----------+------------+----------+-----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+\n",
            "|       310|    20101229|  20110110|   20110105|      21768|           1|         19|                6|         SO43697|                   1|             1|            1|  3578.27|       3578.27|                   0|             0|          2171.2942|       2171.2942|    3578.27|286.2616|89.4568|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1406.9758000000002|3953.9883999999997|          1|\n",
            "|       346|    20101229|  20110110|   20110105|      28389|           1|         39|                7|         SO43698|                   1|             1|            1|  3399.99|       3399.99|                   0|             0|          1912.1544|       1912.1544|    3399.99|271.9992|84.9998|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1487.8355999999999|          3756.989|          1|\n",
            "+----------+------------+----------+-----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+\n",
            "only showing top 2 rows\n",
            "\n",
            "+-----------+--------------------+--------------+\n",
            "|CurrencyKey|CurrencyAlternateKey|  CurrencyName|\n",
            "+-----------+--------------------+--------------+\n",
            "|          1|                 AFA|       Afghani|\n",
            "|          2|                 DZD|Algerian Dinar|\n",
            "+-----------+--------------------+--------------+\n",
            "only showing top 2 rows\n",
            "\n",
            "+----------+-------------------+---------------------+---------------------+-------------------+---------------+------------+-----------------+-----+----------------+------------+---------+----+---------+------+-----------------+-----------+-----------+-----+-----+---------+------------------+-------------------+-------+-------+\n",
            "|ProductKey|ProductAlternateKey|ProductSubcategoryKey|WeightUnitMeasureCode|SizeUnitMeasureCode|    ProductName|StandardCost|FinishedGoodsFlag|Color|SafetyStockLevel|ReorderPoint|ListPrice|Size|SizeRange|Weight|DaysToManufacture|ProductLine|DealerPrice|Class|Style|ModelName|ProductDescription|          StartDate|EndDate| Status|\n",
            "+----------+-------------------+---------------------+---------------------+-------------------+---------------+------------+-----------------+-----+----------------+------------+---------+----+---------+------+-----------------+-----------+-----------+-----+-----+---------+------------------+-------------------+-------+-------+\n",
            "|         1|            AR-5381|                 NULL|                 NULL|               NULL|Adjustable Race|        NULL|                0|   NA|            1000|         750|     NULL|NULL|       NA|  NULL|                0|       NULL|       NULL| NULL| NULL|     NULL|              NULL|2003-07-01 00:00:00|   NULL|Current|\n",
            "|         2|            BA-8327|                 NULL|                 NULL|               NULL|   Bearing Ball|        NULL|                0|   NA|            1000|         750|     NULL|NULL|       NA|  NULL|                0|       NULL|       NULL| NULL| NULL|     NULL|              NULL|2003-07-01 00:00:00|   NULL|Current|\n",
            "+----------+-------------------+---------------------+---------------------+-------------------+---------------+------------+-----------------+-----+----------------+------------+---------+----+---------+------+-----------------+-----------+-----------+-----+-----+---------+------------------+-------------------+-------+-------+\n",
            "only showing top 2 rows\n",
            "\n",
            "+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+----------------+---------------+-----------------+-----------------+----------------+--------------+---------------+---------------+------------+-------------------+-------------------+---------------+\n",
            "|CustomerKey|GeographyKey|CustomerAlternateKey|Title|FirstName|MiddleName|LastName|NameStyle|          BirthDate|MaritalStatus|Suffix|Gender|        EmailAddress|YearlyIncome|TotalChildren|NumberChildrenAtHome|EnglishEducation|SpanishEducation|FrenchEducation|EnglishOccupation|SpanishOccupation|FrenchOccupation|HouseOwnerFlag|NumberCarsOwned|   AddressLine1|AddressLine2|              Phone|  DateFirstPurchase|CommuteDistance|\n",
            "+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+----------------+---------------+-----------------+-----------------+----------------+--------------+---------------+---------------+------------+-------------------+-------------------+---------------+\n",
            "|      11000|          26|          AW00011000| NULL|      Jon|         V|    Yang|        0|1971-10-06 00:00:00|            M|  NULL|     M|jon24@adventure-w...|     90000.0|            2|                   0|       Bachelors|    Licenciatura|        Bac + 4|     Professional|      Profesional|           Cadre|             1|              0|3761 N. 14th St|        NULL|1 (11) 500 555-0162|2011-01-19 00:00:00|      1-2 Miles|\n",
            "|      11001|          37|          AW00011001| NULL|   Eugene|         L|   Huang|        0|1976-05-10 00:00:00|            S|  NULL|     M|eugene10@adventur...|     60000.0|            3|                   3|       Bachelors|    Licenciatura|        Bac + 4|     Professional|      Profesional|           Cadre|             0|              1|     2243 W St.|        NULL|1 (11) 500 555-0110|2011-01-15 00:00:00|      0-1 Miles|\n",
            "+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+----------------+---------------+-----------------+-----------------+----------------+--------------+---------------+---------------+------------+-------------------+-------------------+---------------+\n",
            "only showing top 2 rows\n",
            "\n",
            "+-----------------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "|SalesTerritoryKey|SalesTerritoryAlternateKey|SalesTerritoryRegion|SalesTerritoryCountry|SalesTerritoryGroup| SalesTerritoryImage|\n",
            "+-----------------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "|                1|                         1|           Northwest|        United States|      North America|0xFFD8FFE000104A4...|\n",
            "|                2|                         2|           Northeast|        United States|      North America|0xFFD8FFE000104A4...|\n",
            "+-----------------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "only showing top 2 rows\n",
            "\n",
            "+--------+--------------------+---------------+--------------------+--------------------+-------------------+----------------+---------------+----------------+----------------+----------------+---------------+-----------------+---------------+------------+----------------+-------------+----------+--------------+\n",
            "| DateKey|FullDateAlternateKey|DayNumberOfWeek|EnglishDayNameOfWeek|SpanishDayNameOfWeek|FrenchDayNameOfWeek|DayNumberOfMonth|DayNumberOfYear|WeekNumberOfYear|EnglishMonthName|SpanishMonthName|FrenchMonthName|MonthNumberOfYear|CalendarQuarter|CalendarYear|CalendarSemester|FiscalQuarter|FiscalYear|FiscalSemester|\n",
            "+--------+--------------------+---------------+--------------------+--------------------+-------------------+----------------+---------------+----------------+----------------+----------------+---------------+-----------------+---------------+------------+----------------+-------------+----------+--------------+\n",
            "|20050101| 2005-01-01 00:00:00|              7|            Saturday|              Sábado|             Samedi|               1|              1|               1|         January|           Enero|        Janvier|                1|              1|        2005|               1|            3|      2005|             2|\n",
            "|20050102| 2005-01-02 00:00:00|              1|              Sunday|             Domingo|           Dimanche|               2|              2|               2|         January|           Enero|        Janvier|                1|              1|        2005|               1|            3|      2005|             2|\n",
            "+--------+--------------------+---------------+--------------------+--------------------+-------------------+----------------+---------------+----------------+----------------+----------------+---------------+-----------------+---------------+------------+----------------+-------------+----------+--------------+\n",
            "only showing top 2 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# showing the datasets\n",
        "df_fact.show(2)\n",
        "df_currency.show(2)\n",
        "df_product.show(2)\n",
        "df_customer.show(2)\n",
        "df_Territory.show(2)\n",
        "df_Date.show(2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fact.summary().show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOruBMOtv0TK",
        "outputId": "dbe509de-c5ff-459d-9608-f170ef55d486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+------------------+-------------------+-------------------+--------------------+------------------+------------------+-----------------+------------------+----------------+--------------------+--------------+-------------+-----------------+-----------------+--------------------+--------------+-------------------+-----------------+-----------------+------------------+------------------+---------------------+----------------+------------------+------------------+-------------------+\n",
            "|summary|        ProductKey|       OrderDateKey|         DueDateKey|         ShipDateKey|       CustomerKey|      PromotionKey|      CurrencyKey| SalesTerritoryKey|SalesOrderNumber|SalesOrderLineNumber|RevisionNumber|OrderQuantity|        UnitPrice|   ExtendedAmount|UnitPriceDiscountPct|DiscountAmount|ProductStandardCost| TotalProductCost|      SalesAmount|            TaxAmt|           Freight|CarrierTrackingNumber|CustomerPONumber|            Profit|        TotalSpent|        customerSeg|\n",
            "+-------+------------------+-------------------+-------------------+--------------------+------------------+------------------+-----------------+------------------+----------------+--------------------+--------------+-------------+-----------------+-----------------+--------------------+--------------+-------------------+-----------------+-----------------+------------------+------------------+---------------------+----------------+------------------+------------------+-------------------+\n",
            "|  count|             60398|              60398|              60398|               60398|             60398|             60398|            60398|             60398|           60398|               60398|         60398|        60398|            60398|            60398|               60398|         60398|              60398|            60398|            60398|             60398|             60398|                60398|           60398|             60398|             60398|              60398|\n",
            "|   mean|  437.557932381867|2.012973493314348E7|2.013010416608166E7|2.0129938434186563E7|18841.685420047022|1.0418391337461506|69.84532600417232| 6.244461737143614|            null|  1.8863207390973211|           1.0|          1.0|486.0869105049381|486.0869105049381|                 0.0|           0.0|  286.0656574008799|286.0656574008799|486.0869105049381|38.886953708746155|12.152217111491757|                 null|            null| 200.0212531044196| 537.1260813255993| 0.2517467465810126|\n",
            "| stddev|118.08839049700869| 4745.0503377864325|  4985.453229355979|   4851.060708270001| 5432.430404014208| 0.342604570073712| 42.1463631512379|2.9611497332880754|            null|  1.0163284211738743|           0.0|          0.0| 928.489891980806| 928.489891980806|                 0.0|           0.0|  552.4576408810644|552.4576408810644| 928.489891980806| 74.27919255248143|  23.2122482327679|                 null|            null| 378.9598984781788|1025.9813327660474|0.43402009289402926|\n",
            "|    min|               214|           20101229|           20110110|            20110105|             11000|                 1|                6|                 1|         SO43697|                   1|             1|            1|             2.29|             2.29|                   0|             0|             0.8565|           0.8565|             2.29|            0.1832|            0.0573|                 NULL|            NULL|            1.4335|            2.5305|                  0|\n",
            "|    25%|               359|           20130403|           20130415|            20130410|             14002|                 1|               19|                 4|            null|                   1|             1|            1|             7.95|             7.95|                   0|             0|             2.9733|           2.9733|             7.95|             0.636|            0.1988|                 null|            null|3.1237000000000004|            8.7848|                  0|\n",
            "|    50%|               479|           20130714|           20130726|            20130721|             18143|                 1|              100|                 7|            null|                   2|             1|            1|            29.99|            29.99|                   0|             0|            11.2163|          11.2163|            29.99|            2.3992|            0.7498|                 null|            null|15.643699999999999|33.138999999999996|                  0|\n",
            "|    75%|               529|           20131018|           20131030|            20131025|             23428|                 1|              100|                 9|            null|                   2|             1|            1|           539.99|           539.99|                   0|             0|           294.5797|         294.5797|           539.99|           43.1992|           13.4998|                 null|            null|          196.3404| 596.6890000000001|                  1|\n",
            "|    max|               606|           20140128|           20140209|            20140204|             29483|                14|              100|                10|         SO75123|                   8|             1|            1|          3578.27|          3578.27|                   0|             0|          2171.2942|        2171.2942|          3578.27|          286.2616|           89.4568|                 NULL|            NULL|1487.8355999999999|3953.9883999999997|                  1|\n",
            "+-------+------------------+-------------------+-------------------+--------------------+------------------+------------------+-----------------+------------------+----------------+--------------------+--------------+-------------+-----------------+-----------------+--------------------+--------------+-------------------+-----------------+-----------------+------------------+------------------+---------------------+----------------+------------------+------------------+-------------------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9a9KF4kJM1-"
      },
      "source": [
        "# The Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "b85CAoL7JZY3",
        "outputId": "18f228a3-9e92-49e0-e697-159c9e21e72a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# KDE plot od the Unit price column\n",
        "kde_price = df_fact.select(\"UnitPrice\")\n",
        "plt.figure(figsize=(8,5))\n",
        "kde_price = kde_price.toPandas()\n",
        "sns.kdeplot(kde_price['UnitPrice'], shade = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above is the Kernel Density Distribution plot of the Unit Price column, it appears that the distribution is highly left-skewed. In order words, it indicates that we have majority of the price around 100 per unit"
      ],
      "metadata": {
        "id": "-a4if0GLniPy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "MhD3ObD_JZam",
        "outputId": "10324dcf-0884-4828-d513-c2ccca2d0ad3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#scatter plot\n",
        "TA = df_fact.select('TotalSpent', 'TaxAmt').toPandas()\n",
        "plt.figure(figsize=(5,10))\n",
        "sns.lmplot(x='TaxAmt', y='TotalSpent', data=TA);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scatter plot\n",
        "yt = df_fact.select('TotalProductCost', 'TaxAmt').toPandas()\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.scatterplot(x='TotalProductCost', y='TaxAmt', data=yt, palette = 'winter')\n",
        "plt.title('Product cost vs Tax amount');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "sB8TyPi501Yx",
        "outputId": "6f8d94be-316a-4e9c-f7d7-6006a6f5d171"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9k4iBH0XJxfX",
        "outputId": "cc55cf07-1d8e-405b-dc88-dd5cbbfa2e69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------------+----------+------------+----------+-----------+-----------+------------+-----------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "|SalesTerritoryKey|ProductKey|OrderDateKey|DueDateKey|ShipDateKey|CustomerKey|PromotionKey|CurrencyKey|SalesOrderNumber|SalesOrderLineNumber|RevisionNumber|OrderQuantity|UnitPrice|ExtendedAmount|UnitPriceDiscountPct|DiscountAmount|ProductStandardCost|TotalProductCost|SalesAmount|  TaxAmt|Freight|CarrierTrackingNumber|CustomerPONumber|          OrderDate|            DueDate|           ShipDate|            Profit|        TotalSpent|customerSeg|SalesTerritoryAlternateKey|SalesTerritoryRegion|SalesTerritoryCountry|SalesTerritoryGroup| SalesTerritoryImage|\n",
            "+-----------------+----------+------------+----------+-----------+-----------+------------+-----------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "|                6|       310|    20101229|  20110110|   20110105|      21768|           1|         19|         SO43697|                   1|             1|            1|  3578.27|       3578.27|                   0|             0|          2171.2942|       2171.2942|    3578.27|286.2616|89.4568|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1406.9758000000002|3953.9883999999997|          1|                         6|              Canada|               Canada|      North America|0xFFD8FFE000104A4...|\n",
            "|                7|       346|    20101229|  20110110|   20110105|      28389|           1|         39|         SO43698|                   1|             1|            1|  3399.99|       3399.99|                   0|             0|          1912.1544|       1912.1544|    3399.99|271.9992|84.9998|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1487.8355999999999|          3756.989|          1|                         7|              France|               France|             Europe|0xFFD8FFE000104A4...|\n",
            "+-----------------+----------+------------+----------+-----------+-----------+------------+-----------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------------+--------------------+---------------------+-------------------+--------------------+\n",
            "only showing top 2 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# joining the fact and territory table together\n",
        "fact_terr = df_fact.join(df_Territory, on = ['SalesTerritoryKey'], how = 'left_outer')\n",
        "fact_terr.show(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "1tn1Q8HodxbS",
        "outputId": "96d28a44-5119-4582-f1ed-47021f698c6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+---------------------+------------------+\n",
            "|SalesTerritoryCountry|       avg(Profit)|\n",
            "+---------------------+------------------+\n",
            "|              Germany|211.08813600000246|\n",
            "|               France|195.44165559554006|\n",
            "|        United States|182.76709158077773|\n",
            "|               Canada|108.91358283464798|\n",
            "|            Australia|276.19745794680404|\n",
            "|       United Kingdom| 201.3453197219836|\n",
            "+---------------------+------------------+\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Profit made on sales in each Territory Countey\n",
        "fact_terr.groupby('SalesTerritoryCountry').agg({'Profit' : 'mean'}).show()\n",
        "pro = fact_terr.groupby('SalesTerritoryCountry').agg({'Profit' : 'mean'}).toPandas()\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot( x = pro['SalesTerritoryCountry'], y = pro['avg(Profit)'])\n",
        "plt.xlabel('Sales Territory')\n",
        "plt.ylabel('Profit');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DUGhiOZamFjz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b02410ce-68b4-44fe-92e8-e391a8ae7a15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------+------------------+\n",
            "|SalesTerritoryGroup|Mean UnitPrice    |\n",
            "+-------------------+------------------+\n",
            "|Pacific            |678.9809355115221 |\n",
            "|Europe             |493.67252271557425|\n",
            "|North America      |392.47460201987155|\n",
            "+-------------------+------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "fact_terr.groupBy(\"SalesTerritoryGroup\").agg(mean(\"UnitPrice\").alias(\"Mean UnitPrice\")).sort(desc(\"Mean UnitPrice\")).show(truncate = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kBc1a9hrocOs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95ceff37-3aba-43dc-c157-0ca612656291"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+----------+------------+----------+-----------+-----------+------------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------+---------------+\n",
            "|CurrencyKey|ProductKey|OrderDateKey|DueDateKey|ShipDateKey|CustomerKey|PromotionKey|SalesTerritoryKey|SalesOrderNumber|SalesOrderLineNumber|RevisionNumber|OrderQuantity|UnitPrice|ExtendedAmount|UnitPriceDiscountPct|DiscountAmount|ProductStandardCost|TotalProductCost|SalesAmount|  TaxAmt|Freight|CarrierTrackingNumber|CustomerPONumber|          OrderDate|            DueDate|           ShipDate|            Profit|        TotalSpent|customerSeg|CurrencyAlternateKey|   CurrencyName|\n",
            "+-----------+----------+------------+----------+-----------+-----------+------------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------+---------------+\n",
            "|         19|       310|    20101229|  20110110|   20110105|      21768|           1|                6|         SO43697|                   1|             1|            1|  3578.27|       3578.27|                   0|             0|          2171.2942|       2171.2942|    3578.27|286.2616|89.4568|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1406.9758000000002|3953.9883999999997|          1|                 CAD|Canadian Dollar|\n",
            "|         39|       346|    20101229|  20110110|   20110105|      28389|           1|                7|         SO43698|                   1|             1|            1|  3399.99|       3399.99|                   0|             0|          1912.1544|       1912.1544|    3399.99|271.9992|84.9998|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1487.8355999999999|          3756.989|          1|                 FRF|   French Franc|\n",
            "+-----------+----------+------------+----------+-----------+-----------+------------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+--------------------+---------------+\n",
            "only showing top 2 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# joining the fact and currency table\n",
        "fact_curr = df_fact.join(df_currency, on = ['CurrencyKey'], how = 'left_outer')\n",
        "fact_curr.show(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4jwKTK4nL3Eq"
      },
      "outputs": [],
      "source": [
        "fact_curr.describe().show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coVOLt-TQrvs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "809d3644-580d-4936-fb86-24b67c134ab4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe1157130>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "bar_tax = fact_curr.groupBy(\"CurrencyName\").agg(sum(\"TaxAmt\").alias(\"TaxAmount\")).sort(desc(\"TaxAmount\")).toPandas()\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(data=bar_tax, x=\"TaxAmount\", y=\"CurrencyName\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fg_Tuhe8I0hq",
        "outputId": "b42f8fa2-9e91-450c-a6f0-ab4574390439"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+----------+------------+----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+--------------------+---------------+-----------------+-----------------+----------------+--------------+---------------+------------------+------------+-------------------+-------------------+---------------+\n",
            "|CustomerKey|ProductKey|OrderDateKey|DueDateKey|ShipDateKey|PromotionKey|CurrencyKey|SalesTerritoryKey|SalesOrderNumber|SalesOrderLineNumber|RevisionNumber|OrderQuantity|UnitPrice|ExtendedAmount|UnitPriceDiscountPct|DiscountAmount|ProductStandardCost|TotalProductCost|SalesAmount|  TaxAmt|Freight|CarrierTrackingNumber|CustomerPONumber|          OrderDate|            DueDate|           ShipDate|            Profit|        TotalSpent|customerSeg|GeographyKey|CustomerAlternateKey|Title|FirstName|MiddleName|LastName|NameStyle|          BirthDate|MaritalStatus|Suffix|Gender|        EmailAddress|YearlyIncome|TotalChildren|NumberChildrenAtHome|EnglishEducation|    SpanishEducation|FrenchEducation|EnglishOccupation|SpanishOccupation|FrenchOccupation|HouseOwnerFlag|NumberCarsOwned|      AddressLine1|AddressLine2|              Phone|  DateFirstPurchase|CommuteDistance|\n",
            "+-----------+----------+------------+----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+--------------------+---------------+-----------------+-----------------+----------------+--------------+---------------+------------------+------------+-------------------+-------------------+---------------+\n",
            "|      21768|       310|    20101229|  20110110|   20110105|           1|         19|                6|         SO43697|                   1|             1|            1|  3578.27|       3578.27|                   0|             0|          2171.2942|       2171.2942|    3578.27|286.2616|89.4568|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1406.9758000000002|3953.9883999999997|          1|          53|          AW00021768| NULL|     Cole|         A|  Watson|        0|1952-02-19 00:00:00|            S|  NULL|     M|cole1@adventure-w...|     70000.0|            5|                   0|       Bachelors|        Licenciatura|        Bac + 4|       Management|          Gestión|       Direction|             1|              3|  601 Asilomar Dr.|        NULL|       110-555-0129|2010-12-29 00:00:00|      10+ Miles|\n",
            "|      28389|       346|    20101229|  20110110|   20110105|           1|         39|                7|         SO43698|                   1|             1|            1|  3399.99|       3399.99|                   0|             0|          1912.1544|       1912.1544|    3399.99|271.9992|84.9998|                 NULL|            NULL|2010-12-29 00:00:00|2011-01-10 00:00:00|2011-01-05 00:00:00|1487.8355999999999|          3756.989|          1|         222|          AW00028389| NULL|  Rachael|         M|Martinez|        0|1970-06-17 00:00:00|            S|  NULL|     F|rachael16@adventu...|     20000.0|            3|                   3|     High School|Educación secundaria|        Bac + 2|           Manual|           Obrero|         Ouvrier|             0|              0|14, avenue du Port|        NULL|1 (11) 500 555-0190|2010-12-29 00:00:00|      0-1 Miles|\n",
            "+-----------+----------+------------+----------+-----------+------------+-----------+-----------------+----------------+--------------------+--------------+-------------+---------+--------------+--------------------+--------------+-------------------+----------------+-----------+--------+-------+---------------------+----------------+-------------------+-------------------+-------------------+------------------+------------------+-----------+------------+--------------------+-----+---------+----------+--------+---------+-------------------+-------------+------+------+--------------------+------------+-------------+--------------------+----------------+--------------------+---------------+-----------------+-----------------+----------------+--------------+---------------+------------------+------------+-------------------+-------------------+---------------+\n",
            "only showing top 2 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Joining the fact and customer table\n",
        "fact_cus = df_fact.join(df_customer, on = ['customerKey'], how = 'left_outer')\n",
        "fact_cus.show(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "4nvIPndNKHcn",
        "outputId": "788cccf8-79e0-49e0-b891-54b55fd106de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe09c3430>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# coutplot of occupation of good and bad customer\n",
        "cus_seg = fact_cus.toPandas()\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x ='EnglishOccupation', hue='customerSeg', data= cus_seg)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot of customer segment spending rate speding rate\n",
        "sns.boxplot(x='customerSeg', y='YearlyIncome', data= cus_seg);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "qf4NyhYdA9t6",
        "outputId": "290a7672-1d37-4667-f111-70a042394b40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KhOkQDaHMmss",
        "outputId": "3911f3c7-92dc-411d-f6bf-62df472b2b91"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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jPD8moHhBpLWn3wKKMSbwJmWj/NGovs0LKNNL84fmRa9Q2roHMlImY4wZCwsoAdHQ3ksoO+tlVyJDAaXHAooxJvgsoATEobZeppbm4fU78IRysijIzbaAYoyZFCygBERDey8zyvJfMb+8MNcCijFmUrCAEhANbb1MK31lQCkrsIBijJkcLKAEgKrS0N77sgb5qLKCXFqtUd4YMwlYQAmA9p5BegciTE9Q5VVWkEvPQNhGHTbGBJ4FlABocPegDBdQwHp6GWOCzwJKANS3eUPUJ6zyKrSAYoyZHCygBED0LvlEjfLl7m75tp7+tJbJGGPGygJKADS0eUOtJAoopfne6DjtvYNpLZMxxoyVBZQAaGjvpao4RCjnlW9HTnYWeTlZdPVZQDHGBJsFlAA43N7L1JJXXp1EFeXlWEAxxgSeBZQAaOkeeTTholA2Xf3hNJbIGGPGzgJKALT2DAz15krErlCMMZOBBZQAaOseoGKkgBKygGKMCT4LKBmmqrT2DAx1D06kKC+brr4w3tORjTEmmCygZFhH3yDhiFI+SpVXWJU+G37FGBNgFlAyLPo0xugQK4kU5Xn3oli1lzEmyCygZFh0JOHywpF6eVlAMcYEnwWUDGt1Q6qM2Ciflw1gXYeNMYFmASXDXrpCsSovY8zkZgElw1q7vSuUspF6eVmVlzFmErCAkmGtSTTKh3KyyM0Wq/IyxgSaBZQMa+0ZoCiUnXBgyFh2t7wxJugsoGRYa/fAiD28oopCOXT1W0AxxgSXBZQMa+vpH7FBPip6t7wxxgSVBZQM865QkggooRw6rcrLGBNgKQ0oInK+iOwQkVoRuTbB8jwRucst3yAi82OWXefm7xCR89y8OSLysIg8JyLbROTjMekrReR+Ednp/lekct/80tLdP+I4XlHWhmKMCbqUBRQRyQZ+CFwALAMuFZFlccmuAlpUdTFwE/BNt+4yYDWwHDgfuNnlNwh8SlWXAWcCV8fkeS3woKouAR50rwOvbZSh66OKQtkMRpSBsI3nZYwJplReoawEalV1t6r2A2uBi+LSXASscdP3AqtERNz8tarap6p7gFpgparWq+rTAKraAWwHZiXIaw3wthTtl29U1avyGqHLcFSBuxel27oOG2MCKpUBZRawP+Z1HS+d/F+RRlUHgTZgSjLruuqxU4ANbtY0Va130w3AtESFEpEPikiNiNQ0NjaObY981tUfZnCUkYajCkLe8Cs9FlCMMQE1KRvlRaQY+A3wCVVtj1+u3oNDEj48RFVvUdUVqrqiuro6xSUdWfQu+WS6DRe6gNJtXYeNMQGVyoByAJgT83q2m5cwjYjkAGXAkZHWFZFcvGDyK1X9bUyaQyIyw6WZARz2bU9SZGgcrySqvF4KKHaFYowJplQGlCeBJSKyQERCeI3s6+LSrAOudNMXAw+5q4t1wGrXC2wBsATY6NpXbgW2q+p3RsjrSuAPvu+Rz5IZuj6qINdVeQ1YQDHGBFNOqjJW1UERuQa4D8gGblPVbSJyA1CjquvwgsMdIlILNOMFHVy6u4Hn8Hp2Xa2qYRF5DXAFsEVENrlNfV5V1wPfAO4WkauAF4F3pWrf/BIduj6ZNpRC1yhvbSjGmKBKWUABcCf69XHzvhwz3Qu8c5h1bwRujJv3d0CGSX8EWDXBIqfVWKq8crOF7CyxKi9jTGBNykb5o0VbjxtpOIkrFBGhMDfbGuWNMYFlASWDWrv7KQxlk5eTnVT6glC2taEYYwLLAkoGtSR5U2NUYSjbqryMMYFlASWDWrsHKEuih1dUQSjHGuWNMYFlASWD2nr6x3aFkmtVXsaY4LKAkkHJDl0fVRCyRnljTHBZQMmg1p7kntYYVRjKZiBsIw4bY4LJAkqGeCMNJ/e0xigbINIYE2QWUDKkuz/MQFjH2MvLDWFv7SjGmACygJIhrT3RcbzGcIWSa1coxpjgsoCSIdGh68uSePxvlA1hb4wJMgsoGdLWPfYrlEJrQzHGBJgFlAyJVnlVjOnGRnsmijEmuCygZEhLd/JD10eFsrPIFrGbG40xgWQBJUOiQ9eXjaGXl4jYzY3GmMBKKqCIyG9F5C0iYgHIJ209A+TnZpGfm9xIw1EFNkCkMSagkg0QNwOXATtF5BsicnwKy3RMaO3up3wMPbyiCkPZ1ihvjAmkpAKKqj6gqu8GTgX2Ag+IyGMi8j4RSb7OxgwZ6zheUTZApDEmqJKuwhKRKcB7gQ8AzwDfwwsw96ekZEe58QaUglCOVXkZYwIpqWfKi8jvgOOBO4B/UdV6t+guEalJVeGOZq09/SysKh7zeoXWKG+MCaikAgrwU1VdHztDRPJUtU9VV6SgXEe98V+hvDTicG629ZEwxgRHsmekryaY97ifBTmWqCqtPQOUjacNJXq3vLWjGGMCZsQrFBGZDswCCkTkFEDcolKgMMVlO2r1DkToH4yMq5dX7ACRpfnWH8IYExyjVXmdh9cQPxv4Tsz8DuDzKSrTUa+1x7tLvmJcVyhuCHtrmDfGBMyIAUVV1wBrROQdqvqbNJXpqNfSNfaBIaNeesiWNcwbY4JltCqvy1X1l8B8Eflk/HJV/U6C1cwoolcoYxm6Pqow1waINMYE02hVXkXu/9j7t5phjWfo+ihrlDfGBNVoVV4/cf+/Mp7MReR8vBsgs4Gfqeo34pbnAb8ATgOOAJeo6l637DrgKiAMfExV73PzbwMuBA6r6okxeV0P/CvQ6GZ9Pr6rc1CM52mNUaGcLLLErlCMMcGT7OCQ3xKRUhHJFZEHRaRRRC4fZZ1s4IfABcAy4FIRWRaX7CqgRVUXAzcB33TrLgNWA8uB84GbXX4At7t5idykqie7v0AGE3hppOHx9PLyRhzOsfG8jDGBk+x9KG9S1Xa8K4O9wGLgM6OssxKoVdXdqtoPrAUuiktzEbDGTd8LrBIRcfPXuhsn9wC1Lj9U9VGgOclyB1Jrdz95OVlDDexjVZhrd8sbY4In2YASrRp7C3CPqrYlsc4sYH/M6zo3L2EaVR0E2oApSa6byDUisllEbhORikQJROSDIlIjIjWNjY2JkqTceO+SjyoMZdNtbSjGmIBJNqD8UUSex2vreFBEqoHe1BVrXH4ELAJOBuqBbydKpKq3qOoKVV1RXV2dzvINae0Z39D1UQU2hL0xJoCSHb7+WuDVwApVHQC6eGX1VbwDwJyY17PdvIRpRCQHKMNrnE9m3fgyHlLVsKpGgJ/iqsiCqLV7fMOuRNkzUYwxQTSW0QWXApeIyHuAi4E3jZL+SWCJiCwQkRBeI/u6uDTrgCvd9MXAQ6qqbv5qEckTkQXAEmDjSBsTkRkxL98ObE1inzKirWeA8jE8+jdeQa5VeRljgifZ4evvwKtO2oTXjRdA8br8JqSqgyJyDXAfXrfh21R1m4jcANSo6jrgVuAOEanFa2hf7dbdJiJ3A88Bg8DVqhp2ZbkTOAeoEpE64D9U9VbgWyJysivXXuBDSR+FNGvtHuCk2RMIKKEc+gcjDEYi5GTZiMPGmGBIdvj6FcAyd/WQNNd1d33cvC/HTPcC7xxm3RuBGxPMv3SY9FeMpWyZ1NLdT0Xh+NtQhm5u7A9Tkm8BxRgTDMmejbYC01NZkGNF70CYvsHIhNtQwG5uNMYES7JXKFXAcyKyEeiLzlTVt6akVEexidzUGFUQc4VijDFBkWxAuT6VhTiWRAeGnNB9KLk2hL0xJniSCiiq+lcRmQcsUdUHRKQQr6HdjNFLVygTaZSPDhBpd8sbY4Ij2bG8/hVvaJSfuFmzgN+nqlBHs9ZuN3S9taEYY44yyTbKXw2cDbQDqOpOYGqqCnU0i16hTKSXV54bcdjaUIwxQZJsQOlzAzwCQ3e1j6kLsfFMZOj6KBGxmxuNMYGTbED5q4h8HigQkXOBe4D/TV2xjl6t3QOEsrMoyJ1YE1RBKMeqvIwxgZJsQLkW78FVW/DuQF8PfDFVhTqatfX0U1aYizdK//h543lZo7wxJjiS7eUVEZHfA79X1cyM+X6UaO2e2DheUYWhbNpd9ZkxxgTBiFco4rleRJqAHcAO97TGL4+0nhneRIddibI2FGNM0IxW5fXveL27TlfVSlWtBM4AzhaRf0956Y5CEx26PsqGsDfGBM1oAeUK4FL3GF4AVHU3cDnwnlQW7Gg10aHrowpC2fS5EYeNMSYIRgsouaraFD/TtaNM/Kx4DJro43+jCkJe85ddpRhjgmK0gNI/zmUmgd6BMD0DYcp9aEMptAEijTEBM1ovr38SkfYE8wXIT0F5jmptrldWmR+9vHKj43lZQDHGBMOIAUVVbQBIH/kx7EpUgY3nZYwJGHvcXxpFB4b0ow2lMGRD2BtjgsUCShq1+ljlFR26xe6WN8YEhQWUNGrrnvjAkFH5ud6Iw3ZzozEmKCygpNFLT2uceBuKiJCfazc3GmOCwwJKGrV0D5CTJRSF/OnrUBjKtjYUY0xgWEBJI++mxtCERxqOKrArFGNMgFhASaO2nn5f2k+iCkM5dNtz5Y0xAWEBJY38Gro+ygaINMYEiQWUNGrxaRyvqAJrQzHGBIgFlDRq9elZKFHREYfDEfUtT2OMGS8LKGmiqjR39VNZ5F9AsfG8jDFBktKAIiLni8gOEakVkWsTLM8Tkbvc8g0iMj9m2XVu/g4ROS9m/m0iclhEtsblVSki94vITve/IpX7NlY9A2H6BiNU+BlQhoZfsYZ5Y0zmpSygiEg28EPgAmAZcKmILItLdhXQoqqLgZuAb7p1lwGrgeXA+cDNLj+A2928eNcCD6rqEuBB9zowmru8mxorfa7yAhvC3hgTDKm8QlkJ1KrqblXtB9YCF8WluQhY46bvBVaJd5PGRcBaVe1zT4usdfmhqo8CzQm2F5vXGuBtfu7MRLV0uZGGfb1CsYBijAmOVAaUWcD+mNd1bl7CNKo6CLQBU5JcN940Va130w3AtESJROSDIlIjIjWNjY3J7Icvmt1Iw5VFPvbyyrUh7I0xwXFUNsqrqgIJuz6p6i2qukJVV1RXV6etTC2uysvPXl5DbSjWKG+MCYBUBpQDwJyY17PdvIRpRCQHKAOOJLluvEMiMsPlNQM4PO6Sp8BQG4qPVV55uVkINoS9MSYYUhlQngSWiMgCEQnhNbKvi0uzDrjSTV8MPOSuLtYBq10vsAXAEmDjKNuLzetK4A8+7INvWrr7yRIozfevyitLxG5uNMYERsoCimsTuQa4D9gO3K2q20TkBhF5q0t2KzBFRGqBT+J6ZqnqNuBu4Dngz8DVqhoGEJE7gceB40WkTkSucnl9AzhXRHYCb3SvA6O5y7upMSvLn4Ehowpys+0+FGNMIIz4TPmJUtX1wPq4eV+Ome4F3jnMujcCNyaYf+kw6Y8AqyZS3lRq6e73tYdXlA1hb4wJiqOyUT6Imrv6fb0HJarABog0xgSEBZQ0aekaoMLHLsNRhaEcu1PeGBMIFlDSpLnb33G8ogpC1oZijAkGCyhpoKq0dPk70nBUYW42vQM24rAxJvMsoKRBR98ggxFN2RUK2IjDxpjMs4CSBqm4Sz6qOM/rqNfVZ+0oxpjMsoCSBqm4Sz4qGlA6ei2gGGMyywJKGrS4gSH9fPxvVHG+F1A67QrFGJNhFlDSoKnTCyhTivJ8z7skzwtSFlCMMZlmASUNmjr7AKgq8b/KKz83i+wsobN3wPe8jTFmLCygpEFjRx/FeTlDw837SUQozsuxKxRjTMZZQEmDxo4+qkv8r+6KsoBijAkCCyhp0NjRR3VxagOK9fIyxmSaBZQ0aOzsS0n7SVRJvl2hGGMyzwJKGjSl4Qqlq2+QiNrwK8aYzLGAkmK9A2HaewdT24aSn0NEseeiGGMyygJKikW7DKe6UR7sXhRjTGZZQEmxxo40BJTo3fLWMG+MySALKCk2FFCK81O2jZeuUOzmRmNM5lhASbHosCsp7eUVHX7FrlCMMRlkASXFolcoqRjHK2po+BVrQzHGZJAFlBRr7OylojCXUE7qDrWIUGI3NxpjMswCSoqletiVqNKCXNp6rA3FGJM5FlBSLF0Bpawgl1YLKMaYDLKAkmKNnX1UpfAu+ajyglzaewZQu1veGJMhFlBSKBJRGtp6mVFWkPJtlRXmMhhRuuxueboNH+AAABhbSURBVGNMhlhASaGmzj4Gwsqs8tTdgxJVVuB1HW7rtmovY0xmpDSgiMj5IrJDRGpF5NoEy/NE5C63fIOIzI9Zdp2bv0NEzhstTxG5XUT2iMgm93dyKvctGQdaewCYWZ6GK5RoQOnpT/m2jDEmEf8fIeiISDbwQ+BcoA54UkTWqepzMcmuAlpUdbGIrAa+CVwiIsuA1cByYCbwgIgc59YZKc/PqOq9qdqnsTrY2gukN6BYw7wxJlNSeYWyEqhV1d2q2g+sBS6KS3MRsMZN3wusEhFx89eqap+q7gFqXX7J5BkYB9N4hVKUl0NOlljXYWNMxqQyoMwC9se8rnPzEqZR1UGgDZgywrqj5XmjiGwWkZtEJGHXKhH5oIjUiEhNY2Pj2PdqDA609lCcl0NpfsouBIdkidi9KMaYjDqaGuWvA5YCpwOVwOcSJVLVW1R1haquqK6uTmmBDrb2MLM8H++iK/XKCnKtUd4YkzGpDCgHgDkxr2e7eQnTiEgOUAYcGWHdYfNU1Xr19AE/x6sey6iDbT1pqe6KKrcrFGNMBqUyoDwJLBGRBSISwmtkXxeXZh1wpZu+GHhIvTvz1gGrXS+wBcASYONIeYrIDPdfgLcBW1O4b0k52Nqb1oBSVpBLe++APQrYGJMRKavcV9VBEbkGuA/IBm5T1W0icgNQo6rrgFuBO0SkFmjGCxC4dHcDzwGDwNWqGgZIlKfb5K9EpBoQYBPw4VTtWzJ6+sM0d/UzK50BpTCXiGKDRBpjMiKlrcWquh5YHzfvyzHTvcA7h1n3RuDGZPJ0898w0fL66WBbtIdX6m9qjKoo9J650txl96IYY9LvaGqUD5ShLsNpGHYlqtqNGRZ9jr0xxqSTBZQUOdCSvntQosoKc8nJEgsoxpiMsICSInuaughlZ6U1oGSJUFkUoqnDAooxJv0soKTIrsZOFlQVkZ2VnntQoqqK84aeY2+MMelkASVFdjV2sWhqUdq3W12SR3NXP4PhSNq3bYw5tllASYG+wTD7mrtZVF2c9m1XFYcIqw6NdGyMMeliASUF9h3pJhzRDAUUr6fX7qautG/bGHNss4CSArsaOwEyElCmuICyp9ECijEmvSygpMAudzJfWJ3+NpSiUDb5uVnsbupM+7aNMcc2CygpsOtwJzPK8inKS/2w9fFEhKkl+exo6Ej7to0xx7b0n/GOAbuaujJydRI1q6KAZ/e3Eo5o2rstm8nl1xv2+ZLPZWfM9SUfM7lZQPFZOKLUHurgHafNzlgZZpcX8PiuI9Qe7uT46SUZK4c5urT1DLD1QBsvNnfTPximvCDE4qnFLJtZmumimYCwgOKzXY2ddPWH+afZ5Rkrw6wK7+78Z+taLaCYCevpD/PA84fYuLuZsCqVRSHyc7N48Ug3G/c2M7Ukj4XVRbx6UVWmi2oyzAKKz57Z1wLAyXMzF1CqivMoycthc10r71oxZ/QVjBnG7qZO7qmpo71ngBXzK3ntkqqhrunhiLK9vp0/b2vg8p9t4NoLlvKvr12YtieUmuCxgOKzZ/a1UlaQy4IpmWtDyRLhxFllbK5ry1gZzOSmqjy++wjrt9RTURjiI+csYnZF4cvSZGd5n7Ml04rZuKeZr61/nuauAT53/vEWVI5R1svLZ5v2t3LynHKyMtwYftKcMrbXt9M3GM5oOczkE44of9h0kD9uruf4aSVc8/rFrwgmsfJysrn53ady+Zlz+fFfd/HdB3amsbQmSOwKxUedfYPsONTB+SdOz3RROHl2OQNhZeuBdk6bV5Hp4phJorNvkLUb97G7qYvXHVfNucumkZXE1YaIcMNbT6RvIML3HtzJ/KpC3n5K5jqmmMywgOKjzftbUYVT5mb+BH7WoilkZwkPP3/YAopJSl1LN7/asI+uvkHeedrsMX+Os7KEG9/+Kva3dPO5e7cwt7LIPnvHGKvy8tGGPc2IeFcHmVZeGOK0eRU8sP1QpotiAk5VeXJvMz95dDci8KF/XjTuH0WhnCx+9O7TmFGez4fuqKGupdvn0pogs4Dio788d4gV8yooK8zNdFEAeOMJU3m+ocO+1GZY7b0D/HLDPn73zAEWTCni6nMWD3U7H6+KohC3Xnk6fYMRPrCmhq6+QZ9Ka4LOqrx88uKRLrbXt/PFt5yQ6aIMeeMJ0/ja+ud5cPthrnz1/Ann19zVz5N7m9nd2EVTZx8RVQQhOwsKQzmU5OdQnJdDcfR/zHRJfi5lBcEItAa6+wdZ89iL3PTAC0QiygUnTufsxVVJtZckY/HUYn542am89+cb+cRdm/jJ5adlvKOKST0LKD65b1sDAOctz3yDfNTC6mIWVhXxpy314woov96wj4gqW+ra2Li3mT0xQ+KHcrKInh4iqgyEddT8Zpblc8bCKVxw4nTOOX4qoRy7QE63tu4B1jy+l9sf20tzVz9Lp5fw5lfNGLq3xE//fFw1X7pwGV/53+f49v07+Mx5S33fhgkWCyg++fPWBpbPLGVO5fDdKzPh0pVzuXH99qHuzGPxwqEO/rS5nsbOPiqLQqxaOpXFU4uZXppPXm72y9JGVOkfjNA7EKZvMOL9DYQ5fUElnX2DtHT1s/lAGw/vOMzvnjnA9NJ83v+a+Vy6ci4l+XblkmqH23v52d/38KsnXqSrP8yqpVP56OsXsaMhtaNSv/fV83nhUCc/fHgXS6aW8LZTZqV0e0Hixzhpk22MNAsoPthe387T+1r5zHnHZ7oor3DpGXP5wcO13PxwLbe8Z0VS6xzp7OOGPz7HHzYdpKo4j0tXzmX5zNIRq0OyRMjPzSY/LtC0dg8AUJKfy9mLqjhzwRR2Hurg77VNfG3983z7Ly9w5sIpvGZx1YijMyfzxYpElKbOPnoHImRleSMGxJfnWPPikS5+8uhu7q2pYzAS4cKTZvKRcxZxwgxv/K1UBxQR4StvXc6epk4+c++zVBSFeN1x1Snd5mTV0tXP1oNt7G/poaWrn77BCPc8tZ95lYWcsXAKb1o2beh5R0FlAcUH//PQTkrycrj8jHmZLsorFOflcOVZ8/j+Q7VsrmvlpBF6oKkq9z5Vx9fWb6ezb5A3LJ3KOcdVk5PtX9VUdpawdEYpS2eUUtfSzaM7m3j0hUYe33WEMxZUcvbiKkoTtLUk+rXX0TvArsZOdjd2UdfSQ2NHH2F9edVbRWEucysLWT6zjC9duIyC0LERYLbXt/OjR3bxx80HycnK4uIVs/nQPy9kXgZGcAjlZPGTK1aw+pYn+NAdNdz+vpWcuXBK2ssRRH0DYbYebOPpfa1DVcoVhblUl+RRURSiMJTN32uP8PtNB/nyH7Zy3vLpfGzVEo6bFswx+iygTNCOhg7Wb2ng396wODC9u+K9/zULuOepOj7yy6f5wzVnJ6wvf+FQB1/83VY27m3m1LnlfOMdJ1GztyWl5ZpdUchlK+dyuL2XR15o5O+1TfxjVxPHTSvhhOmlzJ1SSGVRiJwsYSCstHT3U9fSzb7mHl480sXhjj4ACnKzmVNZwHHTSigvzCWUk0U4onT0DtDQ1svOw508W9fG7zcdYNUJ01h9+hzOWjjlqGwkrtnbzM2P7OKh5w9TFMrmA69dyFWvWcC00vyMlqusIJdfvH8ll/70Ca68bSM/uOxUzl02LaNlypSIKrsaO3lmXyvbDrYxEFamFIU4d9k0Tp5TTkVhaCjtZWfMRVV5vqGDe5+q464n9/OnLfVceNJMPvHGJRl5KuxIRHX0xtSj1YoVK7Smpmbc6/cNhll9yxPsPNTJ3z77eiqKQqOvlIRU1L1uqWvj4h8/xvwpRXzt/53IqXMrUIXtDe38asM+7n5yP8X5OVx7/lLetWIOWVni27MykhXtRfbs/lZaewaGTZefm8WcikIWVRezaGoxM8ryR6yOC0eUPU1d9A6G+dPmetp6BphbWcjqlXO4+LTZTC1J/cm2fzBCzd5m/rGrie31HRxs7aGtZ4AsEUoLcplVXsCrZpVx6rxyTp5TnlS7UvT9GQhH2F7fzhO7m9l7pIvCUDavXjSFsxZWBe6KrKtvkNsf28vB1h5WnTCNc46vHnrvJlt7wWhivz8RVepaenjuYBvP1rXR1jNAfm4WJ80q59S55cypLEw4/ln8MWnp6uenf9vN7Y/tpXcgzDtOnc3H37hkxKFxUkFEnlLVV9ShW0AZZ0CJRJTP/24La5/cz4/efSoXvGqGb+VKVWPewzsOc91vttDQ3ktJXg6KN9RGXk4W7zhtNp9+0/FUxgTFdAeUKFWlsaOP+rZeWrr7GQgroeyXTrxVJXnj6t562Rlz6R0Ic9+2Bn69YR8b9jSTkyW88YRpXHrGXF67uMrXq5amzj4e2dHIQ88f4m8vNNHRN0hOlrB4ajEiQmFuNorS3R/mSFc/TR19KCDAjLJ8FlQVsaCqmJnl+ZQV5L7shNPTH2Z/Szfb69vZXNdGz0CY8oJczl5cxenzKwPdg65/MMLvNx1g0/5W5lYWctHJM5lRVnDUBZSbH65l75Eu9jR18XxDBx29g2QJLJlawilzyzlhRim5o1QnD3dMmjr7+NEju7jjiRdB4dKVc7jy1fNZmKYrlowEFBE5H/gekA38TFW/Ebc8D/gFcBpwBLhEVfe6ZdcBVwFh4GOqet9IeYrIAmAtMAV4CrhCVftHKt94A0pzVz+funsTD+9o5KPnLOKz5/vbHTKVvUO6+gb5zdN17DrcSUThVbPKOHfZtIRXV5kKKKkSf0x2NXZy15P7ufepOpq7+plVXsCF/zSDsxdVsWJ+BYWhsdUI9w6EeXpfC0/sOsLfapvY5IbimVaaxxuWTuUNS6dx9uIpFIZyEh7b3oEw+5u7ebG5mz1NXexv7mYw4n0/c7OForwcBOgbjNDd7w36mZMlLJ9ZyqnzKlhUXezbfSSppqo8s6+V9Vvr6ekPc8KMUq5781LOWjjF1za7dGnu6uf5hnZ2NHSwua6NjXuaOdDaA3hX1Iuqi1k+s5Tjp5WO6apxtCB7sLWH7z+4k3ufqmMworx60RRWr/R+HPlVY5JI2gOKiGQDLwDnAnXAk8ClqvpcTJqPAiep6odFZDXwdlW9RESWAXcCK4GZwAPAcW61hHmKyN3Ab1V1rYj8GHhWVX80UhnHG1A+vvYZ/m9LA1+68AQuP3Oe70N1B6W74dEWUIYzGI7wXH07NXtb2N3kBdpsEaaV5TGlKI+q4hClBbmEsrNYdcI0sgS6+gfp6gvT2NHH7qYudjd2svNwJ/2DEbIETppd7oLIVJbPLH3FZySZYzsQjnCgpYeG9l6au/rp6htEgVB2FhVFIWaVFzC3sjDQVyOj6e4f5B+1R3hi9xHvKqswl5XzKzlhRilzKwuZU+m1oxWGsikMeb0I83KyUjI8vqoSUe+/4lWV9vSH6eofpLs/TFffIK3dA9S39dLQ1kN9Wy/1bb28cKhjqD0PvN6FKxdUkCXC/ClFTB+lSnYkyX6PD3f0ck9NHb/esG8okC2dXsLKBZXMn1LErIoCZpUXUJqfS1FeNkV5ORM6jpkIKGcB16vqee71dQCq+vWYNPe5NI+LSA7QAFQD18amjaZzq70iT+AbQCMwXVUH47c9nPEGlMMdvRxu7+PEWWVjXjcZFlAyp28wzItHutnd2El9Wy9Huvpp6epnuG+JCMyuKGBhVTHHTy/hzIWVnD6/ctQ2kGPx2I5kIBxhWmkeD2w/TM3eZl5s7ma4U5OI101dYl4LQnSGxM5z06qgeAGDmOloEBkrEZhaksf00nwWTS3mhOmlHD+9hKUzSqguzkPEnzbIsX6PwxHlmX0tPLH7CE/sbuaZfS109Sd+hMVt713BG5aOr2PEcAEllb28ZgH7Y17XAWcMl8YFgja8KqtZwBNx60bviEqU5xSgVVUHE6R/GRH5IPBB97JTRHaMYZ/SoQpommgm7/ahIBnmy3FIh73A31OX/aQ5DikUyGOwNw3biPse+3ocVn1zQqsnvEfimOs2rKq3ALdkuhzDEZGaRJH/WGPHwWPHwY5B1GQ4DqmsfD0AxD7QfLablzCNq/Iqw2ucH27d4eYfAcpdHsNtyxhjTAqlMqA8CSwRkQUiEgJWA+vi0qwDrnTTFwMPqdeosw5YLSJ5rvfWEmDjcHm6dR52eeDy/EMK980YY0yclFV5uTaRa4D78Lr43qaq20TkBqBGVdcBtwJ3iEgt0IwXIHDp7gaeAwaBq1U1DJAoT7fJzwFrReSrwDMu78kosNVxaWbHwWPHwY5BVOCPwzF9Y6Mxxhj/TN4O7MYYYwLFAooxxhhfWEAJEBE5X0R2iEitiFyb6fL4TURuE5HDIrI1Zl6liNwvIjvd/wo3X0Tk++5YbBaRU2PWudKl3ykiVybaVlCJyBwReVhEnhORbSLycTf/WDsO+SKyUUSedcfhK27+AhHZ4Pb3Ltf5BtdB5y43f4OIzI/J6zo3f4eIjHgzcxCJSLaIPCMif3SvJ+8xUFX7C8AfXieDXcBCIAQ8CyzLdLl83sd/Bk4FtsbM+xZwrZu+Fvimm34z8H94Nz6fCWxw8yuB3e5/hZuuyPS+jeEYzABOddMleEMJLTsGj4MAxW46F9jg9u9uYLWb/2PgI276o8CP3fRq4C43vcx9V/KABe47lJ3p/Rvjsfgk8Gvgj+71pD0GdoUSHCuBWlXdrd6glmuBizJcJl+p6qN4vfliXQSscdNrgLfFzP+Fep7Au89oBnAecL+qNqtqC3A/cH7qS+8PVa1X1afddAewHW9Uh2PtOKiqRh8Xmev+FHgDcK+bH38cosfnXmCVeANRXQSsVdU+Vd0D1OJ9lyYFEZkNvAX4mXstTOJjYAElOBINVXMsPIB7mqrWu+kGIDq40HDH46g5Tq7K4hS8X+fH3HFwVT2bgMN4AXEXww+h9LJhmoDYYZom83H4LvBZIOJejzSMVOCPgQUUExjqXb8fE/3YRaQY+A3wCVVtj112rBwHVQ2r6sl4I1usBPx9DkTAiciFwGFVfSrTZfGLBZTgSGaomqPRIVeFg/t/2M0f6/A7k4aI5OIFk1+p6m/d7GPuOESpaiveSBdnMfwQSmMdpmkyOBt4q4jsxavifgPes54m7TGwgBIcyQxVczSKHX4ndsicdcB7XC+nM4E2VyV0H/AmEalwPaHe5OZNCq7O+1Zgu6p+J2bRsXYcqkWk3E0X4D3jaDvDD6E01mGaAk9Vr1PV2ao6H+/7/pCqvpvJfAwy3cPB/l7W2+PNeL1+dgFfyHR5UrB/dwL1wABePe9VeHXADwI78R6kVunSCvBDdyy2ACti8nk/XsNjLfC+TO/XGI/Ba/CqszYDm9zfm4/B43AS3hBJm4GtwJfd/IV4J8Na4B4gz83Pd69r3fKFMXl9wR2fHcAFmd63cR6Pc3ipl9ekPQY29IoxxhhfWJWXMcYYX1hAMcYY4wsLKMYYY3xhAcUYY4wvLKAYY4zxhQUUY8ZARKaIyCb31yAiB2Jeh+LSfkJECpPI8xERWeGm3y8iW9zIwltFxPfx3ETk837naQzYExuNGTcRuR7oVNX/Hmb5Xrz7RppGyecR4NN4Y3j9FW804jY3PEu1egP++VnuTlUt9jNPY8CuUIyZMBFZ5Z5nsUW8Z77kicjHgJnAwyLysEv3IxGpiX3+R5ypQAfQCaCqndFg4q5ivueuhLaKyEo3v8htc6Mrw0Vu/ntF5Lci8mfxnpfyLTf/G0CBy+dXqT425thiAcWYickHbgcuUdVXATl4z6/4PnAQeL2qvt6l/YKqrsC7S/x1InJSXF7PAoeAPSLycxH5l7jlheoNpvhR4LZonnhDcKwEXg/8l4gUuWUnA5cArwIuEZE5qnot0KOqJ6s3zIcxvrGAYszEZAN7VPUF93oN3oPEEnmXiDyNN+TIcrwHIw1R1TDeM00uxhuC5yZXrRZ1p0v3KFDqxsJ6E3CtGwb+EbwAN9elf1BV21S1F3gOmDeB/TRmVDmjJzHGTJQbtO/TwOmq2iIit+Od/F9GvUbNjcBGEbkf+DlwfXRxfHK8sb7eoao74rZ3BtAXMyuMfd9NitkVijETEwbmi8hi9/oKvIZ18NpDStx0KdAFtInINOCC+IxEZKbEPDMer8rqxZjXl7h0r8EbdbgNb4Thf3OjGCMipyRR5gE3hL4xvrJfLMZMTC/wPuAe94yKJ/GeAw5wC/BnETmoqq8XkWeA5/GervePBHnlAv8tIjNdvo3Ah2O35fLIxRtpGOA/8Z76t1lEsoA9wIWjlPkWl/5pa0cxfrJuw8ZMAtGuxapak+myGDMcq/IyxhjjC7tCMcYY4wu7QjHGGOMLCyjGGGN8YQHFGGOMLyygGGOM8YUFFGOMMb74//wqIY10avYTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "yt = fact_cus.select('YearlyIncome', 'TotalSpent').toPandas()\n",
        "sns.distplot(yt['TotalSpent'], kde=True, bins=15)\n",
        "plt.title('Distribution Plot of Total spent by Customers');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen from the frequency distribution plot above, majority of the customers spent a total amount below 500, which means majority of the customers spend minimally in relation to their earnings"
      ],
      "metadata": {
        "id": "ZvuAwOXfnCmW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "AOtSAP30QVVa",
        "outputId": "8c38fd3f-46a1-40c8-9a77-992f8a2e3992"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sex = fact_cus.select('Gender', 'YearlyIncome').toPandas()\n",
        "plt.figure(figsize=(10,5))\n",
        "sex[sex['Gender'] == 'M']['YearlyIncome'].hist(bins=35, color='blue', alpha= 0.6, label ='Male' )\n",
        "sex[sex['Gender'] == 'F']['YearlyIncome'].hist(bins=35, color='red', alpha= 0.6, label ='Female')\n",
        "#plt.title('Yearly Income')\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "_ZJQAzxqYllJ",
        "outputId": "2af5df26-77e3-4484-e2e9-28afa5baeb1b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Facet Gridplot showing the spending rate of both genders\n",
        "fc = fact_cus.toPandas()\n",
        "g = sns.FacetGrid(data=fc, col='Gender')\n",
        "g.map(sns.distplot, 'TotalSpent', kde=False);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As evident above, the spending rate of house owners is much higher that that of non-house owners, this might have come off from the fact that they have enough financial power to spend more on basic resources."
      ],
      "metadata": {
        "id": "fIGxCpBDmuE9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "f3euDfSpbT3i",
        "outputId": "67e89252-dc6c-4310-a204-d481ad9e766c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Facet Gridplot showing the spending rate of house ownership\n",
        "fc = fact_cus.toPandas()\n",
        "g = sns.FacetGrid(data=fc, col='HouseOwnerFlag')\n",
        "g.map(sns.distplot, 'TotalSpent', kde=False);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similar to the house owners comparison, the above chart comapres spending rate of both sex, but in this case, there is no significant difference in both genders, i.e, they both spend on similar rate."
      ],
      "metadata": {
        "id": "eJpgthuxmlej"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQMu1AGX1snV",
        "outputId": "edaaed5d-3174-450f-cac5-c626a653f9ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9978454558160699"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fact_cus.corr('TotalProductCost', 'TaxAmt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LS55WvQif-IW",
        "outputId": "fcb87290-1d51-4c14-89c7-95352a5642b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-----------------------+----+----+\n",
            "|EnglishEducation_Gender|   F|   M|\n",
            "+-----------------------+----+----+\n",
            "|        Partial College|8318|8305|\n",
            "|        Graduate Degree|5400|5203|\n",
            "|            High School|5040|5280|\n",
            "|              Bachelors|8993|9151|\n",
            "|    Partial High School|2266|2442|\n",
            "+-----------------------+----+----+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Contingency table of customers with English education and their gender \n",
        "fact_cus.crosstab(\"EnglishEducation\", \"Gender\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above is the contingency(cross) tabulation of customers with English education and their respective sex."
      ],
      "metadata": {
        "id": "mCNdbMDJmamR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "t4iM-G7pge0V",
        "outputId": "e250b2e4-5558-4b4d-f54e-80c4ff4611e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corre = fact_cus.select('TotalChildren', 'HouseOwnerFlag', 'NumberCarsOwned', 'YearlyIncome').toPandas().corr()\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(corre, cmap = 'magma', linewidths=1, linecolor='white', annot = True)\n",
        "plt.title('Correlation Heatmap');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ES5sFr0zjkZ2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cUmZ9NhF5VTz"
      },
      "source": [
        "# CLASSIFICATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S3mPAng35a5g"
      },
      "outputs": [],
      "source": [
        "# Joining the tables for Classification Analysis\n",
        "df_join1 = fact_terr.join(df_customer, on = ['CustomerKey'], how = 'left_outer')\n",
        "df_class = df_join1.join(df_product, on = ['ProductKey'], how = 'left_outer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "b83_YEqm6fxs",
        "outputId": "05975a22-66a0-4692-e248-47f4c216f5a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  SalesTerritoryGroup  BirthDate  YearlyIncome  TotalChildren  \\\n",
              "0       North America 1952-02-19       70000.0              5   \n",
              "1              Europe 1970-06-17       20000.0              3   \n",
              "2       North America 1952-06-01       40000.0              5   \n",
              "3       North America 1943-11-10       80000.0              4   \n",
              "4             Pacific 1973-08-14       70000.0              0   \n",
              "\n",
              "   NumberChildrenAtHome EnglishEducation EnglishOccupation  NumberCarsOwned  \\\n",
              "0                     0        Bachelors        Management                3   \n",
              "1                     3      High School            Manual                0   \n",
              "2                     0      High School      Professional                3   \n",
              "3                     0  Graduate Degree        Management                2   \n",
              "4                     0        Bachelors      Professional                1   \n",
              "\n",
              "   customerSeg  \n",
              "0            1  \n",
              "1            1  \n",
              "2            1  \n",
              "3            1  \n",
              "4            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5c88401d-d94b-4e20-a5f2-e4290f525afc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SalesTerritoryGroup</th>\n",
              "      <th>BirthDate</th>\n",
              "      <th>YearlyIncome</th>\n",
              "      <th>TotalChildren</th>\n",
              "      <th>NumberChildrenAtHome</th>\n",
              "      <th>EnglishEducation</th>\n",
              "      <th>EnglishOccupation</th>\n",
              "      <th>NumberCarsOwned</th>\n",
              "      <th>customerSeg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North America</td>\n",
              "      <td>1952-02-19</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Management</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Europe</td>\n",
              "      <td>1970-06-17</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>High School</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>North America</td>\n",
              "      <td>1952-06-01</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>High School</td>\n",
              "      <td>Professional</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>North America</td>\n",
              "      <td>1943-11-10</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate Degree</td>\n",
              "      <td>Management</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pacific</td>\n",
              "      <td>1973-08-14</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Professional</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c88401d-d94b-4e20-a5f2-e4290f525afc')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-5c88401d-d94b-4e20-a5f2-e4290f525afc 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-5c88401d-d94b-4e20-a5f2-e4290f525afc');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# Selecting the needed features\n",
        "classs = df_class.select('SalesTerritoryGroup', 'BirthDate', 'YearlyIncome', 'TotalChildren', 'NumberChildrenAtHome', 'EnglishEducation', 'EnglishOccupation', 'NumberCarsOwned', 'customerSeg').toPandas()\n",
        "classs.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA MANIPULATION"
      ],
      "metadata": {
        "id": "TV-QcF_4Ka9e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ULomkVg9OjZ"
      },
      "outputs": [],
      "source": [
        "# converting the customer date of birth to age\n",
        "from datetime import date\n",
        "classs['BirthDate'] = pd.to_datetime(classs.BirthDate)\n",
        "classs['Customer_Age'] = 2023 - classs['BirthDate'].apply(lambda time: time.year)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "_HRx1xZZ_9g8",
        "outputId": "94d4a168-81e1-444a-80f9-08e0e3c4616e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# using seaborn to create a heatmap to visualise missing values\n",
        "sns.heatmap(classs.isnull());"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3KxeqpKB-ib"
      },
      "source": [
        "The all-rhymed heatmap above inidicates nonNA dataset, so its safe to further with the analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EblZhB5HB8F1"
      },
      "outputs": [],
      "source": [
        "# encoding the string variables\n",
        "STG = pd.get_dummies(classs['SalesTerritoryGroup'], drop_first=True)\n",
        "edu = pd.get_dummies(classs['EnglishEducation'], drop_first=True)\n",
        "occu = pd.get_dummies(classs['EnglishOccupation'], drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPBEw6RAQjSa"
      },
      "outputs": [],
      "source": [
        "# concatenating the new variables to the main and dropping the old ones.\n",
        "classs = pd.concat([classs, STG, edu, occu], axis = 1)\n",
        "classs.drop(columns=['SalesTerritoryGroup', 'EnglishEducation', 'EnglishOccupation', 'BirthDate'], inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classs = classs[['YearlyIncome',\t'TotalChildren',\t'NumberChildrenAtHome',\t'NumberCarsOwned',\t'customerSeg',\t'Customer_Age',\t'North America',\t'Pacific',\t'High School',\t'Partial High School',\t'Management',\t'Skilled Manual']]\n",
        "classs.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "ekNKkH-Lt_2-",
        "outputId": "ade1d9a9-32ce-43d2-9ec1-837b10acf678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearlyIncome  TotalChildren  NumberChildrenAtHome  NumberCarsOwned  \\\n",
              "0       70000.0              5                     0                3   \n",
              "1       20000.0              3                     3                0   \n",
              "\n",
              "   customerSeg  Customer_Age  North America  Pacific  High School  \\\n",
              "0            1            71              1        0            0   \n",
              "1            1            53              0        0            1   \n",
              "\n",
              "   Partial High School  Management  Skilled Manual  \n",
              "0                    0           1               0  \n",
              "1                    0           0               0  "
            ],
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              "      <th>YearlyIncome</th>\n",
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              "      <th>NumberCarsOwned</th>\n",
              "      <th>customerSeg</th>\n",
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              "      <th>High School</th>\n",
              "      <th>Partial High School</th>\n",
              "      <th>Management</th>\n",
              "      <th>Skilled Manual</th>\n",
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              "          const element = document.querySelector('#df-993f0e0c-6f77-4938-b38d-13e8a82638a2');\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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            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.distplot(classs['Customer_Age'], kde=True, bins= 25);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "6wPdr_kReLZr",
        "outputId": "ddb8998a-3909-4e27-9af7-1f968bd52500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "wCtSmaXoKtUz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EuRjih7oUuHx"
      },
      "outputs": [],
      "source": [
        "# creating the independent and dependent variables\n",
        "X = classs.drop(['customerSeg'], axis = 1)\n",
        "y = classs['customerSeg']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B_22hLlJd17Q"
      },
      "outputs": [],
      "source": [
        "# Splitting the dataset into test and train\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling out the dataset\n",
        "scaler_X = StandardScaler()\n",
        "X_train = scaler_X.fit_transform(X_train)\n",
        "X_test = scaler_X.fit_transform(X_test)"
      ],
      "metadata": {
        "id": "YEaZijmBk-8u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LOGISTIC REGRESSION"
      ],
      "metadata": {
        "id": "k1tYvfxBlI0A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a logistic regression object\n",
        "logmodel = LogisticRegression()"
      ],
      "metadata": {
        "id": "4uaIADK_lhad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logmodel.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQfF5_Wallly",
        "outputId": "5c6fc676-b727-4365-fe45-310a995c76fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_pred = logmodel.predict(X_test)"
      ],
      "metadata": {
        "id": "IBUznc_plpJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "print(confusion_matrix(y_test, log_pred))\n",
        "print('\\n')\n",
        "print(classification_report(y_test, log_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GuQezDWlzzP",
        "outputId": "9e47cad4-86ce-4ca9-8de8-bcdc3c03e9be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6710   47]\n",
            " [2254   49]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.99      0.85      6757\n",
            "           1       0.51      0.02      0.04      2303\n",
            "\n",
            "    accuracy                           0.75      9060\n",
            "   macro avg       0.63      0.51      0.45      9060\n",
            "weighted avg       0.69      0.75      0.65      9060\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SUPPORT VECTOR MACHINE"
      ],
      "metadata": {
        "id": "P1r1L8elLN3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = SVC(kernel='rbf', random_state =0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hlks_K-Umt6F",
        "outputId": "51d3bb80-89fb-4436-88cd-ba7c220012b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "Ir9ZUv3kmwMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, svc_pred))\n",
        "print('\\n')\n",
        "print(classification_report(y_test, svc_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3Gfj_xVnT19",
        "outputId": "fec68b96-6f9c-48ea-b9be-10c7a6504848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6720   37]\n",
            " [2270   33]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.99      0.85      6757\n",
            "           1       0.47      0.01      0.03      2303\n",
            "\n",
            "    accuracy                           0.75      9060\n",
            "   macro avg       0.61      0.50      0.44      9060\n",
            "weighted avg       0.68      0.75      0.64      9060\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NAIV BAYES"
      ],
      "metadata": {
        "id": "Y7TBNmhYAJxd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zutLBkEqoKVG",
        "outputId": "96621c6c-b3c7-463f-d90b-a622d9069850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "adLFpMnWoLdp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, nb_pred))\n",
        "print('\\n')\n",
        "print(classification_report(y_test, nb_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZFes-z6RoOwm",
        "outputId": "7cf109c8-372b-434f-86e8-c3f84d6a06b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10166  1132]\n",
            " [ 3102   700]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.90      0.83     11298\n",
            "           1       0.38      0.18      0.25      3802\n",
            "\n",
            "    accuracy                           0.72     15100\n",
            "   macro avg       0.57      0.54      0.54     15100\n",
            "weighted avg       0.67      0.72      0.68     15100\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOTE: all libraries used are uploaded in the earlier part of this book"
      ],
      "metadata": {
        "id": "2Z0JglTsHmm3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BFnBUcyGH6Xi"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}