KPMG_Data_Analytics_Virtual_Experience_Program.ipynb

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    "colab": {
      "name": "KPMG Data Analytics Virtual Experience Program ",
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      "authorship_tag": "ABX9TyOrxUINzGWny+SPvaHPUt34",
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        "<a href=\"https://colab.research.google.com/github/vithika-karan/KPMG-Virtual-Internship/blob/main/KPMG_Data_Analytics_Virtual_Experience_Program.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "##Problem Statement\n",
        "Sprocket Central Pty Ltd , a medium size bikes & cycling accessories organization has approached KPMG. They are looking to expand into a new market and we will be assisting them by showcasing our data analytics skills to recommend a suitable marketing strategy."
      ],
      "metadata": {
        "id": "gct1ntryfojH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The client provided KPMG with 3 datasets:\n",
        "\n",
        "* Customer Demographic \n",
        "* Customer Addresses\n",
        "* Transactions data in the past 3 months\n"
      ],
      "metadata": {
        "id": "gn0QHLZrf4hA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data Quality Assessment"
      ],
      "metadata": {
        "id": "5uEULlJ6gibD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import date\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.rcParams.update({'figure.figsize':(10,5),'figure.dpi':100})\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "lwlFEV3If-7c"
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      "execution_count": 378,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer Demographics"
      ],
      "metadata": {
        "id": "dWzkInKBg_pq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 379,
      "metadata": {
        "id": "rxCUw97UeU5e"
      },
      "outputs": [],
      "source": [
        "#reading excel sheet\n",
        "xls = (\"/content/drive/MyDrive/KPMG/RAW DATA/KPMG_VI_New_raw_data_update_final.xlsx\")\n",
        "df_demography = pd.read_excel(xls, sheet_name=3,header=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#first look\n",
        "df_demography.head()"
      ],
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          "height": 374
        },
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              "      <th></th>\n",
              "      <th>customer_id</th>\n",
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              "      <th>past_3_years_bike_related_purchases</th>\n",
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              "      <th>deceased_indicator</th>\n",
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              "      <th>owns_car</th>\n",
              "      <th>tenure</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
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              "      <td>Recruiting Manager</td>\n",
              "      <td>Property</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>Talbot</td>\n",
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              "      <td>1961-10-03</td>\n",
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              "      <td>IT</td>\n",
              "      <td>Mass Customer</td>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Sheila-kathryn</td>\n",
              "      <td>Calton</td>\n",
              "      <td>Female</td>\n",
              "      <td>56</td>\n",
              "      <td>1977-05-13</td>\n",
              "      <td>Senior Editor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Affluent Customer</td>\n",
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              "   customer_id      first_name  ... owns_car tenure\n",
              "0            1         Laraine  ...      Yes   11.0\n",
              "1            2             Eli  ...      Yes   16.0\n",
              "2            3           Arlin  ...      Yes   15.0\n",
              "3            4          Talbot  ...       No    7.0\n",
              "4            5  Sheila-kathryn  ...      Yes    8.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
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      "cell_type": "code",
      "source": [
        "#info\n",
        "df_demography.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4000 entries, 0 to 3999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   customer_id                          4000 non-null   int64         \n",
            " 1   first_name                           4000 non-null   object        \n",
            " 2   last_name                            3875 non-null   object        \n",
            " 3   gender                               4000 non-null   object        \n",
            " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
            " 5   DOB                                  3913 non-null   datetime64[ns]\n",
            " 6   job_title                            3494 non-null   object        \n",
            " 7   job_industry_category                3344 non-null   object        \n",
            " 8   wealth_segment                       4000 non-null   object        \n",
            " 9   deceased_indicator                   4000 non-null   object        \n",
            " 10  default                              3698 non-null   object        \n",
            " 11  owns_car                             4000 non-null   object        \n",
            " 12  tenure                               3913 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2), object(9)\n",
            "memory usage: 406.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#null values\n",
        "df_demography.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uX3UogAZladK",
        "outputId": "cecf7e79-c093-48f6-b1d2-bb2a7a9d27b0"
      },
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                              0\n",
              "first_name                               0\n",
              "last_name                              125\n",
              "gender                                   0\n",
              "past_3_years_bike_related_purchases      0\n",
              "DOB                                     87\n",
              "job_title                              506\n",
              "job_industry_category                  656\n",
              "wealth_segment                           0\n",
              "deceased_indicator                       0\n",
              "default                                302\n",
              "owns_car                                 0\n",
              "tenure                                  87\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 382
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#duplicates\n",
        "df_demography.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZlneMa_e7AUM",
        "outputId": "726b90ec-af76-4845-9869-37d92d338685"
      },
      "execution_count": 383,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 383
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#earlier we saw a corrupted column in dataset head\n",
        "df_demography.drop('default',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "vTTw2VZ77YHX"
      },
      "execution_count": 384,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#no of unique values\n",
        "df_demography.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBcWwRAH8K7n",
        "outputId": "054ae0be-66ca-434e-f236-1d01531434da"
      },
      "execution_count": 385,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                            4000\n",
              "first_name                             3139\n",
              "last_name                              3725\n",
              "gender                                    6\n",
              "past_3_years_bike_related_purchases     100\n",
              "DOB                                    3448\n",
              "job_title                               195\n",
              "job_industry_category                     9\n",
              "wealth_segment                            3\n",
              "deceased_indicator                        2\n",
              "owns_car                                  2\n",
              "tenure                                   22\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 385
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#gender\n",
        "df_demography['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bOWnpOP8QR2",
        "outputId": "c3e96c60-f9ec-449d-dbd5-04796e171033"
      },
      "execution_count": 386,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    2037\n",
              "Male      1872\n",
              "U           88\n",
              "F            1\n",
              "M            1\n",
              "Femal        1\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 386
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing the inconsistent data\n",
        "df_demography['gender'].replace({'U':'Unspecified','F':'Female','Femal':'Female','M':'Male'},inplace=True)"
      ],
      "metadata": {
        "id": "SpJI-t4s8t0c"
      },
      "execution_count": 387,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let's check dob\n",
        "df_demography['DOB'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQmZyJU-9SGH",
        "outputId": "c68a0101-2309-410e-83fd-9301ce6dbf70"
      },
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "count                    3913\n",
              "unique                   3448\n",
              "top       1978-01-30 00:00:00\n",
              "freq                        7\n",
              "first     1843-12-21 00:00:00\n",
              "last      2002-03-11 00:00:00\n",
              "Name: DOB, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 388
        }
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    {
      "cell_type": "code",
      "source": [
        "df_demography[df_demography['DOB'] < '1931-01-01 00:00:00']"
      ],
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        "id": "oA2f7-hW9p_g",
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      "execution_count": 389,
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              "      <th>owns_car</th>\n",
              "      <th>tenure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>Jephthah</td>\n",
              "      <td>Bachmann</td>\n",
              "      <td>Unspecified</td>\n",
              "      <td>59</td>\n",
              "      <td>1843-12-21</td>\n",
              "      <td>Legal Assistant</td>\n",
              "      <td>IT</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf1f5df1-5a39-4d11-9258-686dbd445264')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cf1f5df1-5a39-4d11-9258-686dbd445264 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-cf1f5df1-5a39-4d11-9258-686dbd445264');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    customer_id first_name last_name  ... deceased_indicator  owns_car tenure\n",
              "33           34   Jephthah  Bachmann  ...                  N        No   20.0\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 389
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography.loc[33,'DOB']= df_demography.loc[33,'DOB'].replace(year=1977) #replaced it with datetime mean"
      ],
      "metadata": {
        "id": "WdBHuspnCyBg"
      },
      "execution_count": 390,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#job title\n",
        "df_demography['job_title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6H1P_JxuDcZm",
        "outputId": "3ad47d28-e89d-4c85-d3a3-5621e10fb394"
      },
      "execution_count": 391,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business Systems Development Analyst    45\n",
              "Tax Accountant                          44\n",
              "Social Worker                           44\n",
              "Internal Auditor                        42\n",
              "Legal Assistant                         41\n",
              "                                        ..\n",
              "Geologist II                             4\n",
              "Health Coach III                         3\n",
              "Research Assistant III                   3\n",
              "Health Coach I                           3\n",
              "Developer I                              1\n",
              "Name: job_title, Length: 195, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 391
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography['job_title'].fillna(method='bfill',inplace=True)"
      ],
      "metadata": {
        "id": "-JKl3C-UEDYd"
      },
      "execution_count": 392,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#job industry category\n",
        "df_demography['job_industry_category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeNBbjrDEzJ3",
        "outputId": "2185c4b4-8ac3-4760-c77f-393f2a89b865"
      },
      "execution_count": 393,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Manufacturing         799\n",
              "Financial Services    774\n",
              "Health                602\n",
              "Retail                358\n",
              "Property              267\n",
              "IT                    223\n",
              "Entertainment         136\n",
              "Argiculture           113\n",
              "Telecommunications     72\n",
              "Name: job_industry_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 393
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography['job_industry_category'].fillna(method='ffill',inplace=True)"
      ],
      "metadata": {
        "id": "V3QUurOcFAWt"
      },
      "execution_count": 394,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#wealth segment\n",
        "df_demography['wealth_segment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5n93ihpFEhL",
        "outputId": "44d5cb98-455c-4287-c169-49679e163e77"
      },
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mass Customer        2000\n",
              "High Net Worth       1021\n",
              "Affluent Customer     979\n",
              "Name: wealth_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#deceased indicator\n",
        "df_demography['deceased_indicator'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvz_DhmrFhkK",
        "outputId": "fcf29efb-468c-4a48-a4ec-fed89e45104c"
      },
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    3998\n",
              "Y       2\n",
              "Name: deceased_indicator, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 396
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography['deceased_indicator'].replace({'N':False,'Y':True},inplace=True)"
      ],
      "metadata": {
        "id": "tFg3NZoMFxiB"
      },
      "execution_count": 397,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#owns car\n",
        "df_demography['owns_car'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6avjJOBF72I",
        "outputId": "3b12d6e5-5060-4ccb-9ac7-936b3a299c51"
      },
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    2024\n",
              "No     1976\n",
              "Name: owns_car, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 398
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography['owns_car'].replace({'Yes':True,'No':False},inplace=True)"
      ],
      "metadata": {
        "id": "t3JOJybkGGzw"
      },
      "execution_count": 399,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#tenure\n",
        "sns.distplot(x=df_demography['tenure']) #kind of normal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "cYtQviv2GQ3t",
        "outputId": "3b447484-f693-4f85-bdf0-ee7c9560375a"
      },
      "execution_count": 400,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0d8d79e50>"
            ]
          },
          "metadata": {},
          "execution_count": 400
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography['tenure'].fillna(df_demography['tenure'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "_TCQF4pGGfN4"
      },
      "execution_count": 401,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_demography.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlB9rSBUG0Ym",
        "outputId": "8e0eac12-eb54-44d4-87f5-dbe0b6c16088"
      },
      "execution_count": 402,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4000 entries, 0 to 3999\n",
            "Data columns (total 12 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   customer_id                          4000 non-null   int64         \n",
            " 1   first_name                           4000 non-null   object        \n",
            " 2   last_name                            3875 non-null   object        \n",
            " 3   gender                               4000 non-null   object        \n",
            " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
            " 5   DOB                                  3913 non-null   datetime64[ns]\n",
            " 6   job_title                            4000 non-null   object        \n",
            " 7   job_industry_category                4000 non-null   object        \n",
            " 8   wealth_segment                       4000 non-null   object        \n",
            " 9   deceased_indicator                   4000 non-null   bool          \n",
            " 10  owns_car                             4000 non-null   bool          \n",
            " 11  tenure                               4000 non-null   float64       \n",
            "dtypes: bool(2), datetime64[ns](1), float64(1), int64(2), object(6)\n",
            "memory usage: 320.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original_customers = df_demography.copy()"
      ],
      "metadata": {
        "id": "2ARBGmvqB_JC"
      },
      "execution_count": 403,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Customer Addresses**\n"
      ],
      "metadata": {
        "id": "tOnya4VJHe3p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#reading data\n",
        "df_addresses = pd.read_excel(xls, sheet_name=4,header=1)"
      ],
      "metadata": {
        "id": "xl-gOuRLHZ7J"
      },
      "execution_count": 404,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_addresses.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9qnC0nMRIGMp",
        "outputId": "2c27ad6c-bbfe-456e-fb3a-ffec39f15925"
      },
      "execution_count": 405,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-35082bc5-3eb5-4494-bbc3-7a6adfb7b04d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>address</th>\n",
              "      <th>postcode</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>6 Meadow Vale Court</td>\n",
              "      <td>2153</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0 Holy Cross Court</td>\n",
              "      <td>4211</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>17979 Del Mar Point</td>\n",
              "      <td>2448</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>9 Oakridge Court</td>\n",
              "      <td>3216</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35082bc5-3eb5-4494-bbc3-7a6adfb7b04d')\"\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",
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              "    <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",
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              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-35082bc5-3eb5-4494-bbc3-7a6adfb7b04d 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-35082bc5-3eb5-4494-bbc3-7a6adfb7b04d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   customer_id              address  ...    country property_valuation\n",
              "0            1   060 Morning Avenue  ...  Australia                 10\n",
              "1            2  6 Meadow Vale Court  ...  Australia                 10\n",
              "2            4   0 Holy Cross Court  ...  Australia                  9\n",
              "3            5  17979 Del Mar Point  ...  Australia                  4\n",
              "4            6     9 Oakridge Court  ...  Australia                  9\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 405
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#no nulls\n",
        "df_addresses.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIY9n2qFIKF2",
        "outputId": "2c71a95b-2929-4189-8b40-99ba7da2ae80"
      },
      "execution_count": 406,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3999 entries, 0 to 3998\n",
            "Data columns (total 6 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   customer_id         3999 non-null   int64 \n",
            " 1   address             3999 non-null   object\n",
            " 2   postcode            3999 non-null   int64 \n",
            " 3   state               3999 non-null   object\n",
            " 4   country             3999 non-null   object\n",
            " 5   property_valuation  3999 non-null   int64 \n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 187.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_addresses.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaVHuRWcKdA_",
        "outputId": "14e63b6c-9211-424f-e2e3-836e7af6d490"
      },
      "execution_count": 407,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 407
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#unique\n",
        "df_addresses.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2xVt-roIT-N",
        "outputId": "49ab0cd9-62b9-4ec8-bc7c-bcf34ae33d34"
      },
      "execution_count": 408,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id           3999\n",
              "address               3996\n",
              "postcode               873\n",
              "state                    5\n",
              "country                  1\n",
              "property_valuation      12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 408
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing other values\n",
        "df_addresses['state'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SaaA31cGJJab",
        "outputId": "95f1603a-05be-4bfb-f3d3-e6d9fea380a4"
      },
      "execution_count": 409,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NSW                2054\n",
              "VIC                 939\n",
              "QLD                 838\n",
              "New South Wales      86\n",
              "Victoria             82\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 409
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_addresses['state'].replace({'New South Wales':'NSW','Victoria':'VIC'},inplace=True)"
      ],
      "metadata": {
        "id": "Rm2ZlhIWJTFW"
      },
      "execution_count": 410,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer_address = df_addresses.copy()"
      ],
      "metadata": {
        "id": "HZVBw6y1DZO4"
      },
      "execution_count": 411,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Everything else looks fine!"
      ],
      "metadata": {
        "id": "pFwJALEFJ2Gn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**New Customer List**"
      ],
      "metadata": {
        "id": "FO9MKCtwJ96O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#reading data\n",
        "df_ncustomers = pd.read_excel(xls, sheet_name=2,header=1)"
      ],
      "metadata": {
        "id": "7yM32IkmJkWf"
      },
      "execution_count": 412,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "T9sTVusfKMBS",
        "outputId": "94219e7c-3ff7-4b6c-8c6e-a2a7cff7d79e"
      },
      "execution_count": 413,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-456230b7-9a40-42d1-a736-11484574ddef\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_name</th>\n",
              "      <th>last_name</th>\n",
              "      <th>gender</th>\n",
              "      <th>past_3_years_bike_related_purchases</th>\n",
              "      <th>DOB</th>\n",
              "      <th>job_title</th>\n",
              "      <th>job_industry_category</th>\n",
              "      <th>wealth_segment</th>\n",
              "      <th>deceased_indicator</th>\n",
              "      <th>owns_car</th>\n",
              "      <th>tenure</th>\n",
              "      <th>address</th>\n",
              "      <th>postcode</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Unnamed: 19</th>\n",
              "      <th>Unnamed: 20</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chickie</td>\n",
              "      <td>Brister</td>\n",
              "      <td>Male</td>\n",
              "      <td>86</td>\n",
              "      <td>1957-07-12</td>\n",
              "      <td>General Manager</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
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              "      <td>14</td>\n",
              "      <td>45 Shopko Center</td>\n",
              "      <td>4500</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
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              "      <td>0.63</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Morly</td>\n",
              "      <td>Genery</td>\n",
              "      <td>Male</td>\n",
              "      <td>69</td>\n",
              "      <td>1970-03-22</td>\n",
              "      <td>Structural Engineer</td>\n",
              "      <td>Property</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>16</td>\n",
              "      <td>14 Mccormick Park</td>\n",
              "      <td>2113</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>11</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.5600</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.595000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ardelis</td>\n",
              "      <td>Forrester</td>\n",
              "      <td>Female</td>\n",
              "      <td>10</td>\n",
              "      <td>1974-08-28</td>\n",
              "      <td>Senior Cost Accountant</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>10</td>\n",
              "      <td>5 Colorado Crossing</td>\n",
              "      <td>3505</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Australia</td>\n",
              "      <td>5</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.9400</td>\n",
              "      <td>0.940000</td>\n",
              "      <td>0.940000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lucine</td>\n",
              "      <td>Stutt</td>\n",
              "      <td>Female</td>\n",
              "      <td>64</td>\n",
              "      <td>1979-01-28</td>\n",
              "      <td>Account Representative III</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5</td>\n",
              "      <td>207 Annamark Plaza</td>\n",
              "      <td>4814</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.7875</td>\n",
              "      <td>0.787500</td>\n",
              "      <td>0.787500</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.703125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Melinda</td>\n",
              "      <td>Hadlee</td>\n",
              "      <td>Female</td>\n",
              "      <td>34</td>\n",
              "      <td>1965-09-21</td>\n",
              "      <td>Financial Analyst</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>19</td>\n",
              "      <td>115 Montana Place</td>\n",
              "      <td>2093</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.8000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.703125</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-456230b7-9a40-42d1-a736-11484574ddef');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  first_name  last_name  gender  ...  Unnamed: 20 Rank     Value\n",
              "0    Chickie    Brister    Male  ...            1    1  1.718750\n",
              "1      Morly     Genery    Male  ...            1    1  1.718750\n",
              "2    Ardelis  Forrester  Female  ...            1    1  1.718750\n",
              "3     Lucine      Stutt  Female  ...            4    4  1.703125\n",
              "4    Melinda     Hadlee  Female  ...            4    4  1.703125\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 413
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#info\n",
        "df_ncustomers.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8T7AGToKO7y",
        "outputId": "99e9be90-5e54-43a9-b8c6-ad39fd62dd2d"
      },
      "execution_count": 414,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 23 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   first_name                           1000 non-null   object        \n",
            " 1   last_name                            971 non-null    object        \n",
            " 2   gender                               1000 non-null   object        \n",
            " 3   past_3_years_bike_related_purchases  1000 non-null   int64         \n",
            " 4   DOB                                  983 non-null    datetime64[ns]\n",
            " 5   job_title                            894 non-null    object        \n",
            " 6   job_industry_category                835 non-null    object        \n",
            " 7   wealth_segment                       1000 non-null   object        \n",
            " 8   deceased_indicator                   1000 non-null   object        \n",
            " 9   owns_car                             1000 non-null   object        \n",
            " 10  tenure                               1000 non-null   int64         \n",
            " 11  address                              1000 non-null   object        \n",
            " 12  postcode                             1000 non-null   int64         \n",
            " 13  state                                1000 non-null   object        \n",
            " 14  country                              1000 non-null   object        \n",
            " 15  property_valuation                   1000 non-null   int64         \n",
            " 16  Unnamed: 16                          1000 non-null   float64       \n",
            " 17  Unnamed: 17                          1000 non-null   float64       \n",
            " 18  Unnamed: 18                          1000 non-null   float64       \n",
            " 19  Unnamed: 19                          1000 non-null   float64       \n",
            " 20  Unnamed: 20                          1000 non-null   int64         \n",
            " 21  Rank                                 1000 non-null   int64         \n",
            " 22  Value                                1000 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(5), int64(6), object(11)\n",
            "memory usage: 179.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#null values\n",
        "df_ncustomers.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBQfegZUKYWP",
        "outputId": "9470500d-f444-49ea-e2c7-3ba79ab6bdca"
      },
      "execution_count": 415,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_name                               0\n",
              "last_name                               29\n",
              "gender                                   0\n",
              "past_3_years_bike_related_purchases      0\n",
              "DOB                                     17\n",
              "job_title                              106\n",
              "job_industry_category                  165\n",
              "wealth_segment                           0\n",
              "deceased_indicator                       0\n",
              "owns_car                                 0\n",
              "tenure                                   0\n",
              "address                                  0\n",
              "postcode                                 0\n",
              "state                                    0\n",
              "country                                  0\n",
              "property_valuation                       0\n",
              "Unnamed: 16                              0\n",
              "Unnamed: 17                              0\n",
              "Unnamed: 18                              0\n",
              "Unnamed: 19                              0\n",
              "Unnamed: 20                              0\n",
              "Rank                                     0\n",
              "Value                                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 415
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#duplicates\n",
        "df_ncustomers.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogyIfT6-KsVO",
        "outputId": "cf552249-9b3e-4203-f006-9d81b46fc036"
      },
      "execution_count": 416,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 416
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers.nunique()"
      ],
      "metadata": {
        "id": "ZvFV8CUhwMHU",
        "outputId": "22815a41-8052-42ed-b36a-56e22f8e51ad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 417,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_name                              940\n",
              "last_name                               961\n",
              "gender                                    3\n",
              "past_3_years_bike_related_purchases     100\n",
              "DOB                                     958\n",
              "job_title                               184\n",
              "job_industry_category                     9\n",
              "wealth_segment                            3\n",
              "deceased_indicator                        1\n",
              "owns_car                                  2\n",
              "tenure                                   23\n",
              "address                                1000\n",
              "postcode                                522\n",
              "state                                     3\n",
              "country                                   1\n",
              "property_valuation                       12\n",
              "Unnamed: 16                              71\n",
              "Unnamed: 17                             129\n",
              "Unnamed: 18                             182\n",
              "Unnamed: 19                             318\n",
              "Unnamed: 20                             324\n",
              "Rank                                    324\n",
              "Value                                   324\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 417
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers.drop(['Unnamed: 16','Unnamed: 17','Unnamed: 18','Unnamed: 19','Unnamed: 20'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "pg1UcJPvI4VK"
      },
      "execution_count": 418,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#some missing last names in new customer list\n",
        "df_ncustomers['last_name'] = df_ncustomers.groupby('first_name').last_name.bfill().ffill()"
      ],
      "metadata": {
        "id": "Lp1yJb1CJYWs"
      },
      "execution_count": 419,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#gender\n",
        "df_ncustomers['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUsJxg8KK5P6",
        "outputId": "b3874e6a-0dc0-4ee8-d6b6-2da5cfb96a9c"
      },
      "execution_count": 420,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    513\n",
              "Male      470\n",
              "U          17\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 420
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['gender'].replace({'U':'Unspecified'},inplace=True)"
      ],
      "metadata": {
        "id": "PT6w5mXVOnP4"
      },
      "execution_count": 421,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dob\n",
        "df_ncustomers['DOB'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NX-T6aZFLdkz",
        "outputId": "f5c68c23-6783-451d-efc2-6957c53653e3"
      },
      "execution_count": 422,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                     983\n",
              "unique                    958\n",
              "top       1961-07-31 00:00:00\n",
              "freq                        2\n",
              "first     1938-06-08 00:00:00\n",
              "last      2002-02-27 00:00:00\n",
              "Name: DOB, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 422
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['DOB'].fillna(lambda x: np.random.choice(df_ncustomers['DOB']), inplace=True)"
      ],
      "metadata": {
        "id": "ZW_LFgBbJve3"
      },
      "execution_count": 423,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['state'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkwI9iUrMAKP",
        "outputId": "ff8b9890-75e8-4d88-9898-8be752353cd6"
      },
      "execution_count": 424,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NSW    506\n",
              "VIC    266\n",
              "QLD    228\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 424
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['job_title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_u70-GMKO4A",
        "outputId": "850afc6f-3d56-4d62-bd0f-a37c9ef1d98e"
      },
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Associate Professor             15\n",
              "Environmental Tech              14\n",
              "Software Consultant             14\n",
              "Chief Design Engineer           13\n",
              "Assistant Media Planner         12\n",
              "                                ..\n",
              "Budget/Accounting Analyst II     1\n",
              "Accounting Assistant II          1\n",
              "Database Administrator IV        1\n",
              "Administrative Officer           1\n",
              "Safety Technician II             1\n",
              "Name: job_title, Length: 184, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 425
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['job_title']= df_ncustomers['job_title'].fillna(method='bfill')"
      ],
      "metadata": {
        "id": "tyqNMW4pKWJX"
      },
      "execution_count": 426,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['job_industry_category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AGZmOEFKey5",
        "outputId": "21aa328e-c614-4a25-f123-86829a612cf6"
      },
      "execution_count": 427,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Financial Services    203\n",
              "Manufacturing         199\n",
              "Health                152\n",
              "Retail                 78\n",
              "Property               64\n",
              "IT                     51\n",
              "Entertainment          37\n",
              "Argiculture            26\n",
              "Telecommunications     25\n",
              "Name: job_industry_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 427
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ncustomers['job_industry_category']= df_ncustomers['job_industry_category'].fillna(method='ffill')"
      ],
      "metadata": {
        "id": "LWFI_LcXKl8l"
      },
      "execution_count": 428,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers = df_ncustomers.copy()"
      ],
      "metadata": {
        "id": "2adIk-buKxbA"
      },
      "execution_count": 429,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Transactions**\n"
      ],
      "metadata": {
        "id": "4GPdGJboM2R1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#reading data\n",
        "df_transactions = pd.read_excel(xls, sheet_name=1,header=1)"
      ],
      "metadata": {
        "id": "xqLNFr7mMj1J"
      },
      "execution_count": 430,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "H7L4I6ybNF6i",
        "outputId": "1d57a27f-cdb8-4c60-bb11-b081a7bc0e4c"
      },
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-33933fbc-c0e2-4f2e-8e74-a8a52f95fac1\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th>transaction_id</th>\n",
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              "      <th>online_order</th>\n",
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              "      <td>2017-02-25</td>\n",
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              "      <td>Standard</td>\n",
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              "      <td>medium</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>2017-10-16</td>\n",
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              "      <td>OHM Cycles</td>\n",
              "      <td>Standard</td>\n",
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              "      <td>medium</td>\n",
              "      <td>1793.43</td>\n",
              "      <td>248.82</td>\n",
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              "    [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-33933fbc-c0e2-4f2e-8e74-a8a52f95fac1 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-33933fbc-c0e2-4f2e-8e74-a8a52f95fac1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   transaction_id  product_id  ...  standard_cost product_first_sold_date\n",
              "0               1           2  ...          53.62                 41245.0\n",
              "1               2           3  ...         388.92                 41701.0\n",
              "2               3          37  ...         248.82                 36361.0\n",
              "3               4          88  ...         381.10                 36145.0\n",
              "4               5          78  ...         709.48                 42226.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptQY5fv1NAR5",
        "outputId": "d454e92b-36fd-44e7-dda0-e3ee0c617dc9"
      },
      "execution_count": 432,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20000 entries, 0 to 19999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   transaction_id           20000 non-null  int64         \n",
            " 1   product_id               20000 non-null  int64         \n",
            " 2   customer_id              20000 non-null  int64         \n",
            " 3   transaction_date         20000 non-null  datetime64[ns]\n",
            " 4   online_order             19640 non-null  float64       \n",
            " 5   order_status             20000 non-null  object        \n",
            " 6   brand                    19803 non-null  object        \n",
            " 7   product_line             19803 non-null  object        \n",
            " 8   product_class            19803 non-null  object        \n",
            " 9   product_size             19803 non-null  object        \n",
            " 10  list_price               20000 non-null  float64       \n",
            " 11  standard_cost            19803 non-null  float64       \n",
            " 12  product_first_sold_date  19803 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4jAOtuDNORL",
        "outputId": "299253a2-08b0-4687-935a-d19ab47edefc"
      },
      "execution_count": 433,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id               0\n",
              "product_id                   0\n",
              "customer_id                  0\n",
              "transaction_date             0\n",
              "online_order               360\n",
              "order_status                 0\n",
              "brand                      197\n",
              "product_line               197\n",
              "product_class              197\n",
              "product_size               197\n",
              "list_price                   0\n",
              "standard_cost              197\n",
              "product_first_sold_date    197\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 433
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XgOagW5NFDw",
        "outputId": "718beb6a-5456-431c-ccf8-c60ac51101ae"
      },
      "execution_count": 434,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id             20000\n",
              "product_id                   101\n",
              "customer_id                 3494\n",
              "transaction_date             364\n",
              "online_order                   2\n",
              "order_status                   2\n",
              "brand                          6\n",
              "product_line                   4\n",
              "product_class                  3\n",
              "product_size                   3\n",
              "list_price                   296\n",
              "standard_cost                103\n",
              "product_first_sold_date      100\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 434
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADcc9i0GNncS",
        "outputId": "0f95ff94-2dbb-4041-8c5e-3ecc57941591"
      },
      "execution_count": 435,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 435
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sort values\n",
        "df_transactions = df_transactions.sort_values(['customer_id', 'transaction_date'])"
      ],
      "metadata": {
        "id": "1AlU1xLUb-BA"
      },
      "execution_count": 436,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['online_order'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb_4P8eYFtTj",
        "outputId": "e6299f2d-fcfc-46db-89e2-59349a7e6afe"
      },
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    9829\n",
              "0.0    9811\n",
              "Name: online_order, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 437
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['online_order'] = df_transactions['online_order'].fillna(method='bfill')"
      ],
      "metadata": {
        "id": "GZbVXkI1d8pG"
      },
      "execution_count": 438,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['brand'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6_4h9scGWhn",
        "outputId": "019005fe-695b-4cf9-f9d4-58ff0f5ca583"
      },
      "execution_count": 439,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Solex             4253\n",
              "Giant Bicycles    3312\n",
              "WeareA2B          3295\n",
              "OHM Cycles        3043\n",
              "Trek Bicycles     2990\n",
              "Norco Bicycles    2910\n",
              "Name: brand, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 439
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['brand'] = df_transactions['brand'].fillna(method='ffill')"
      ],
      "metadata": {
        "id": "KYrzS2qaeD6k"
      },
      "execution_count": 440,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_line'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TvvdybFCGeGP",
        "outputId": "8d003efd-156b-46de-aeb1-7f013c272367"
      },
      "execution_count": 441,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Standard    14176\n",
              "Road         3970\n",
              "Touring      1234\n",
              "Mountain      423\n",
              "Name: product_line, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 441
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_line'] = df_transactions['product_line'].fillna(method='bfill')"
      ],
      "metadata": {
        "id": "21dYF_s4eM82"
      },
      "execution_count": 442,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuwR_NO_Glu0",
        "outputId": "95328809-478d-48e2-d0f4-4b11ab96037e"
      },
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medium    13826\n",
              "high       3013\n",
              "low        2964\n",
              "Name: product_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_class'] = df_transactions['product_class'].fillna(method='bfill')"
      ],
      "metadata": {
        "id": "pXjzxqxxeWo9"
      },
      "execution_count": 444,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_size'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6uTCNE9GuQ_",
        "outputId": "e16db5f5-8708-477d-dc14-78dee535beb0"
      },
      "execution_count": 445,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medium    12990\n",
              "large      3976\n",
              "small      2837\n",
              "Name: product_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 445
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_size'] = df_transactions['product_size'].fillna(method='ffill')"
      ],
      "metadata": {
        "id": "A9c0y3tVelEQ"
      },
      "execution_count": 446,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['standard_cost'] = df_transactions['standard_cost'].fillna(df_transactions['standard_cost'].mean())"
      ],
      "metadata": {
        "id": "U7sFdCoRetP-"
      },
      "execution_count": 447,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions['product_first_sold_date'] = df_transactions['product_first_sold_date'].fillna(df_transactions['product_first_sold_date'].mean())"
      ],
      "metadata": {
        "id": "9dQT8ibSfP-2"
      },
      "execution_count": 448,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_transactions.info()"
      ],
      "metadata": {
        "id": "NgRDJCgSfZNK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39a2ef26-3f03-4d12-d8a0-2bd7a055a31e"
      },
      "execution_count": 449,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20000 entries, 9784 to 8707\n",
            "Data columns (total 13 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   transaction_id           20000 non-null  int64         \n",
            " 1   product_id               20000 non-null  int64         \n",
            " 2   customer_id              20000 non-null  int64         \n",
            " 3   transaction_date         20000 non-null  datetime64[ns]\n",
            " 4   online_order             20000 non-null  float64       \n",
            " 5   order_status             20000 non-null  object        \n",
            " 6   brand                    20000 non-null  object        \n",
            " 7   product_line             20000 non-null  object        \n",
            " 8   product_class            20000 non-null  object        \n",
            " 9   product_size             20000 non-null  object        \n",
            " 10  list_price               20000 non-null  float64       \n",
            " 11  standard_cost            20000 non-null  float64       \n",
            " 12  product_first_sold_date  20000 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_transactions = df_transactions.copy()"
      ],
      "metadata": {
        "id": "xcXT9h7yHAoz"
      },
      "execution_count": 450,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "Hne6CmiyLEH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data = original_customers.merge(customer_address,on='customer_id')"
      ],
      "metadata": {
        "id": "n8j8pmyfHJC_"
      },
      "execution_count": 451,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data = customer_data.merge(customer_transactions, on = 'customer_id')"
      ],
      "metadata": {
        "id": "3xhZ5dBeLdex"
      },
      "execution_count": 452,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we have all original customer data in one df with transaction history\n",
        "customer_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "H2iUcg62LqjJ",
        "outputId": "94fd96e2-9bff-4c86-d3df-c17882ee24cf"
      },
      "execution_count": 453,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a76d2bdb-bf69-41c7-b986-d7e400dc18ac\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>first_name</th>\n",
              "      <th>last_name</th>\n",
              "      <th>gender</th>\n",
              "      <th>past_3_years_bike_related_purchases</th>\n",
              "      <th>DOB</th>\n",
              "      <th>job_title</th>\n",
              "      <th>job_industry_category</th>\n",
              "      <th>wealth_segment</th>\n",
              "      <th>deceased_indicator</th>\n",
              "      <th>owns_car</th>\n",
              "      <th>tenure</th>\n",
              "      <th>address</th>\n",
              "      <th>postcode</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>transaction_date</th>\n",
              "      <th>online_order</th>\n",
              "      <th>order_status</th>\n",
              "      <th>brand</th>\n",
              "      <th>product_line</th>\n",
              "      <th>product_class</th>\n",
              "      <th>product_size</th>\n",
              "      <th>list_price</th>\n",
              "      <th>standard_cost</th>\n",
              "      <th>product_first_sold_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
              "      <td>Female</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>9785</td>\n",
              "      <td>72</td>\n",
              "      <td>2017-01-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Norco Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>360.40</td>\n",
              "      <td>270.30</td>\n",
              "      <td>37873.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
              "      <td>Female</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>13424</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-02-21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Solex</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>71.49</td>\n",
              "      <td>53.62</td>\n",
              "      <td>38573.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
              "      <td>Female</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>14486</td>\n",
              "      <td>23</td>\n",
              "      <td>2017-03-27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Norco Bicycles</td>\n",
              "      <td>Mountain</td>\n",
              "      <td>low</td>\n",
              "      <td>small</td>\n",
              "      <td>688.63</td>\n",
              "      <td>612.88</td>\n",
              "      <td>33455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
              "      <td>Female</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>18970</td>\n",
              "      <td>11</td>\n",
              "      <td>2017-03-29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Giant Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>high</td>\n",
              "      <td>medium</td>\n",
              "      <td>1274.93</td>\n",
              "      <td>764.96</td>\n",
              "      <td>35378.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
              "      <td>Female</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>3765</td>\n",
              "      <td>38</td>\n",
              "      <td>2017-04-06</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Solex</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>1577.53</td>\n",
              "      <td>826.51</td>\n",
              "      <td>39526.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a76d2bdb-bf69-41c7-b986-d7e400dc18ac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-a76d2bdb-bf69-41c7-b986-d7e400dc18ac 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-a76d2bdb-bf69-41c7-b986-d7e400dc18ac');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   customer_id first_name  ... standard_cost product_first_sold_date\n",
              "0            1    Laraine  ...        270.30                 37873.0\n",
              "1            1    Laraine  ...         53.62                 38573.0\n",
              "2            1    Laraine  ...        612.88                 33455.0\n",
              "3            1    Laraine  ...        764.96                 35378.0\n",
              "4            1    Laraine  ...        826.51                 39526.0\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 453
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9tfZTiyL3FB",
        "outputId": "90e3d9b5-2c27-4a5d-c5e8-347aec0acfc5"
      },
      "execution_count": 454,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 19968 entries, 0 to 19967\n",
            "Data columns (total 29 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   customer_id                          19968 non-null  int64         \n",
            " 1   first_name                           19968 non-null  object        \n",
            " 2   last_name                            19326 non-null  object        \n",
            " 3   gender                               19968 non-null  object        \n",
            " 4   past_3_years_bike_related_purchases  19968 non-null  int64         \n",
            " 5   DOB                                  19522 non-null  datetime64[ns]\n",
            " 6   job_title                            19968 non-null  object        \n",
            " 7   job_industry_category                19968 non-null  object        \n",
            " 8   wealth_segment                       19968 non-null  object        \n",
            " 9   deceased_indicator                   19968 non-null  bool          \n",
            " 10  owns_car                             19968 non-null  bool          \n",
            " 11  tenure                               19968 non-null  float64       \n",
            " 12  address                              19968 non-null  object        \n",
            " 13  postcode                             19968 non-null  int64         \n",
            " 14  state                                19968 non-null  object        \n",
            " 15  country                              19968 non-null  object        \n",
            " 16  property_valuation                   19968 non-null  int64         \n",
            " 17  transaction_id                       19968 non-null  int64         \n",
            " 18  product_id                           19968 non-null  int64         \n",
            " 19  transaction_date                     19968 non-null  datetime64[ns]\n",
            " 20  online_order                         19968 non-null  float64       \n",
            " 21  order_status                         19968 non-null  object        \n",
            " 22  brand                                19968 non-null  object        \n",
            " 23  product_line                         19968 non-null  object        \n",
            " 24  product_class                        19968 non-null  object        \n",
            " 25  product_size                         19968 non-null  object        \n",
            " 26  list_price                           19968 non-null  float64       \n",
            " 27  standard_cost                        19968 non-null  float64       \n",
            " 28  product_first_sold_date              19968 non-null  float64       \n",
            "dtypes: bool(2), datetime64[ns](2), float64(5), int64(6), object(14)\n",
            "memory usage: 4.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "k10F18KMMTRf",
        "outputId": "50f16706-19aa-4d93-89d3-7c17b2509b8b"
      },
      "execution_count": 455,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3bb027e7-ec72-4825-b6b3-93b265543650\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>past_3_years_bike_related_purchases</th>\n",
              "      <th>tenure</th>\n",
              "      <th>postcode</th>\n",
              "      <th>property_valuation</th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>online_order</th>\n",
              "      <th>list_price</th>\n",
              "      <th>standard_cost</th>\n",
              "      <th>product_first_sold_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "      <td>19968.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1740.254307</td>\n",
              "      <td>48.757412</td>\n",
              "      <td>10.681392</td>\n",
              "      <td>2987.623347</td>\n",
              "      <td>7.516376</td>\n",
              "      <td>9997.846504</td>\n",
              "      <td>45.365986</td>\n",
              "      <td>0.501102</td>\n",
              "      <td>1107.780004</td>\n",
              "      <td>556.090619</td>\n",
              "      <td>38198.751474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1009.819066</td>\n",
              "      <td>28.611359</td>\n",
              "      <td>5.610897</td>\n",
              "      <td>851.306647</td>\n",
              "      <td>2.824783</td>\n",
              "      <td>5773.929973</td>\n",
              "      <td>30.750115</td>\n",
              "      <td>0.500011</td>\n",
              "      <td>582.852548</td>\n",
              "      <td>403.979251</td>\n",
              "      <td>2860.784340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.010000</td>\n",
              "      <td>7.210000</td>\n",
              "      <td>33259.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>861.750000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4997.750000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>575.270000</td>\n",
              "      <td>215.140000</td>\n",
              "      <td>35667.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1739.500000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>10.657041</td>\n",
              "      <td>2767.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9996.500000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1163.890000</td>\n",
              "      <td>507.580000</td>\n",
              "      <td>38206.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2614.250000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3754.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14996.250000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1635.300000</td>\n",
              "      <td>795.100000</td>\n",
              "      <td>40672.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3500.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>4883.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>20000.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2091.470000</td>\n",
              "      <td>1759.850000</td>\n",
              "      <td>42710.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bb027e7-ec72-4825-b6b3-93b265543650')\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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-3bb027e7-ec72-4825-b6b3-93b265543650 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-3bb027e7-ec72-4825-b6b3-93b265543650');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        customer_id  ...  product_first_sold_date\n",
              "count  19968.000000  ...             19968.000000\n",
              "mean    1740.254307  ...             38198.751474\n",
              "std     1009.819066  ...              2860.784340\n",
              "min        1.000000  ...             33259.000000\n",
              "25%      861.750000  ...             35667.000000\n",
              "50%     1739.500000  ...             38206.000000\n",
              "75%     2614.250000  ...             40672.000000\n",
              "max     3500.000000  ...             42710.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 455
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cZE92RFMmiR",
        "outputId": "74d3f2f5-daed-4dd5-9ffd-1f816526dc33"
      },
      "execution_count": 456,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 456
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6podjDBMCti",
        "outputId": "26d66b5a-e798-481b-f496-7bfd365f6f39"
      },
      "execution_count": 457,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                              0\n",
              "first_name                               0\n",
              "last_name                              642\n",
              "gender                                   0\n",
              "past_3_years_bike_related_purchases      0\n",
              "DOB                                    446\n",
              "job_title                                0\n",
              "job_industry_category                    0\n",
              "wealth_segment                           0\n",
              "deceased_indicator                       0\n",
              "owns_car                                 0\n",
              "tenure                                   0\n",
              "address                                  0\n",
              "postcode                                 0\n",
              "state                                    0\n",
              "country                                  0\n",
              "property_valuation                       0\n",
              "transaction_id                           0\n",
              "product_id                               0\n",
              "transaction_date                         0\n",
              "online_order                             0\n",
              "order_status                             0\n",
              "brand                                    0\n",
              "product_line                             0\n",
              "product_class                            0\n",
              "product_size                             0\n",
              "list_price                               0\n",
              "standard_cost                            0\n",
              "product_first_sold_date                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 457
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data['last_name'].fillna(method='ffill', inplace = True)\n",
        "customer_data['DOB'].fillna(method='ffill', inplace = True)"
      ],
      "metadata": {
        "id": "Tocj3o-IMI3v"
      },
      "execution_count": 458,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJGB4RQo4DSG",
        "outputId": "41acb016-bca1-4871-d56d-4931019f7248"
      },
      "execution_count": 459,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 19968 entries, 0 to 19967\n",
            "Data columns (total 29 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   customer_id                          19968 non-null  int64         \n",
            " 1   first_name                           19968 non-null  object        \n",
            " 2   last_name                            19968 non-null  object        \n",
            " 3   gender                               19968 non-null  object        \n",
            " 4   past_3_years_bike_related_purchases  19968 non-null  int64         \n",
            " 5   DOB                                  19968 non-null  datetime64[ns]\n",
            " 6   job_title                            19968 non-null  object        \n",
            " 7   job_industry_category                19968 non-null  object        \n",
            " 8   wealth_segment                       19968 non-null  object        \n",
            " 9   deceased_indicator                   19968 non-null  bool          \n",
            " 10  owns_car                             19968 non-null  bool          \n",
            " 11  tenure                               19968 non-null  float64       \n",
            " 12  address                              19968 non-null  object        \n",
            " 13  postcode                             19968 non-null  int64         \n",
            " 14  state                                19968 non-null  object        \n",
            " 15  country                              19968 non-null  object        \n",
            " 16  property_valuation                   19968 non-null  int64         \n",
            " 17  transaction_id                       19968 non-null  int64         \n",
            " 18  product_id                           19968 non-null  int64         \n",
            " 19  transaction_date                     19968 non-null  datetime64[ns]\n",
            " 20  online_order                         19968 non-null  float64       \n",
            " 21  order_status                         19968 non-null  object        \n",
            " 22  brand                                19968 non-null  object        \n",
            " 23  product_line                         19968 non-null  object        \n",
            " 24  product_class                        19968 non-null  object        \n",
            " 25  product_size                         19968 non-null  object        \n",
            " 26  list_price                           19968 non-null  float64       \n",
            " 27  standard_cost                        19968 non-null  float64       \n",
            " 28  product_first_sold_date              19968 non-null  float64       \n",
            "dtypes: bool(2), datetime64[ns](2), float64(5), int64(6), object(14)\n",
            "memory usage: 4.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Engineering**"
      ],
      "metadata": {
        "id": "4wEBBx2ZFHUT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating an age column \n",
        "def age(DOB):\n",
        "  today = date.today()\n",
        "  age = today.year - DOB.year -((today.month, today.day) < (DOB.month, DOB.day))\n",
        "  return age"
      ],
      "metadata": {
        "id": "IDcTAxJcM6QG"
      },
      "execution_count": 460,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data['age'] = customer_data['DOB'].apply(lambda x: age(x))\n",
        "customer_data.drop('DOB',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Dyuisiv9wrPp"
      },
      "execution_count": 461,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "H8LiPRmT3zgH",
        "outputId": "7a1bb08f-63cb-4db7-9563-bf8ab99bd2c9"
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      "execution_count": 462,
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        {
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              "      <td>Laraine</td>\n",
              "      <td>Medendorp</td>\n",
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              "      <td>93</td>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Health</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>11.0</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>9785</td>\n",
              "      <td>72</td>\n",
              "      <td>2017-01-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Norco Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>360.4</td>\n",
              "      <td>270.3</td>\n",
              "      <td>37873.0</td>\n",
              "      <td>68</td>\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   customer_id first_name  ... product_first_sold_date age\n",
              "0            1    Laraine  ...                 37873.0  68\n",
              "\n",
              "[1 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 462
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#profit\n",
        "customer_data['profit'] = customer_data['list_price'] - customer_data['standard_cost']"
      ],
      "metadata": {
        "id": "ir35YG-lEElh"
      },
      "execution_count": 463,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#transaction month\n",
        "customer_data['transaction_month'] = pd.DatetimeIndex(customer_data['transaction_date']).month"
      ],
      "metadata": {
        "id": "6UymcSvTFbLm"
      },
      "execution_count": 464,
      "outputs": []
    },
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      "execution_count": 465,
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              "          const element = document.querySelector('#df-cbafa054-6dde-4ab1-9d11-9ff9a538faab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   customer_id first_name  last_name  ... age  profit transaction_month\n",
              "0            1    Laraine  Medendorp  ...  68    90.1                 1\n",
              "\n",
              "[1 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 465
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_data['online_order'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFFe-5EbHWWr",
        "outputId": "8a3616a8-a615-441c-997d-4f28fd3dc07f"
      },
      "execution_count": 466,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    10006\n",
              "0.0     9962\n",
              "Name: online_order, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 466
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization**"
      ],
      "metadata": {
        "id": "QsBryk2aGRXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_variables = ['gender','job_industry_category','wealth_segment', 'deceased_indicator','owns_car', 'state','country','online_order','order_status','brand','product_line','product_class','product_size','transaction_month']\n",
        "numerical_variables = ['past_3_years_bike_related_purchases','tenure','property_valuation','profit','age']"
      ],
      "metadata": {
        "id": "kyVuOLpLGgmi"
      },
      "execution_count": 467,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def countplots(cat_list):\n",
        "  for elem in cat_list:\n",
        "    plt.rcParams.update({'figure.figsize':(8,5),'figure.dpi':100})\n",
        "    sns.countplot(x = customer_data[elem])\n",
        "    plt.show()\n",
        "\n",
        "countplots(categorical_variables)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TNcG-irnFzJ5",
        "outputId": "107ecc58-b575-44b1-a201-e03f2210fd96"
      },
      "execution_count": 470,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mZvagb1k1E61zliRJ0jpsSofkQUB+NPC8qvrNChy2HXAPcGO/fy6wU3+uMfOAK6vqt4OaXZvzzOvbJUmStJ4Z6XKLJJsAjxo0bd0/eeJm4Abg3+ke//ZCYIMkY2uEb66qpUnmAs8AzgJupfug3THA5wcB+AvAu4HjkxwJbEv3NIu3D677IeB/khwEnAq8Cngq8AYkSZK03hn1muSn0gXcMWPriz8LHAa8uN+/sDnuOcDZdGuCX9XXzqT7gN4xg/NQVYuS7AYcC5wP3AQcXlXHDWq+m2Qv4D3Ae4GfAntW1aWreoOSJEmafkb9nOSz6T6MN5Flfhquqi4AdliB61wMPHs5NV8Fvrq8c0mSJGndN6XXJEuSJEmjYEiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGpMKyUm+mWTzcdpnJfnmqg9LkiRJGp3JziTvAmw0Tvt9gWdPejSSJEnSFDBjZYqTPHGw+7gkcwb7GwC7A79aHQOTJEmSRmWlQjJwIVD9Nt6yijuBN6/qoCRJkqRRWtmQvDUQ4OfA04FfD/qWAjdW1d2raWySJEnSSKxUSK6qa/offSqGJEmS1lkrO5P8B0keDTwH2IImNFfV4as4LkmSJGlkJhWSk/wN8DHgJmAB3RrlMQUYkiVJkjRtTXYm+R+Bf6iqI1fnYCRJkqSpYLJrix8AfHV1DkSSJEmaKiYbkr8K7LY6ByJJkiRNFZNdbnEV8M9JdgAuAX437Kyqf13VgUmSJEmjMtmQ/AbgNmDnfhsqwJAsSZKkaWtSIbmqtl7dA5EkSZKmCr8URJIkSWpM9jnJn1pWf1XtO7nhSJIkSaM32TXJD2j2NwS2BTYHvrlKI5IkSZJGbLJrkl/atiW5D9238P1sVQclSZIkjdJqW5NcVfcARwNvX13nlCRJkkZhdX9w75FMfgmHJEmSNCVMKiQnObrZjknyJeDL/bai59kpySlJrk9SSfZs+pPk8CQ3JLkzyZlJHt3UPDDJiUkWJ7klyfFJNmlqnpjkW0nuSnJtkkPGGcvLk1zR11ySZI+V+1ORJEnSumKyM8lPbrYn9u0HAW9bifNsDFwE7D9B/yHAW4D9gGcAtwPzk9x3UHMi8HhgHvBCYCfguLHOJLOAM4BrgO2Bg4HDkrxhULMj8EXg+P5+TgZOTrLtStyLJEmS1hGT/eDec1bHxavqNOA0gCT36kvX8DbgPVX1H33bq4GFwJ7Al5I8FtgdeFpV/bCveTPw9SR/W1XXA3sDGwH7VtVS4MdJtgMO5I9h+q3A6VV1VL//ziTzgAPoArokSZLWI6u0JjnJg5M8q98evLoG1dsamAOcOdZQVYuA84C5fdNc4JaxgNw7E7iHbuZ5rOacPiCPmQ9sk+QBg5ozubf5g+v8iSQzk8wa24BNV+bmJEmSNHVNdk3yxv0XitwAnNNv1/frge+/msY2p39d2LQvHPTNAW4cdlbV74Gbm5rxzsEK1MxhYocCiwbbdcuolSRJ0jQy2Znko4GdgRfRfYHI5sBL+rYPrJ6hTXlHAJsNtoeNdjiSJElaXSb7uLaXAX9VVWcP2r6e5E7gK8AbV3VgwIL+dTbdjDWD/QsHNVsMD0oyA3jg4PgF/TFDswd9y6pZwASqagmwZHDdiUolSZI0zUx2Jvn+/OnyBOiWPqyu5RZX04XUXcca+rW/zwDO7ZvOBTZPsv3guOfS3dd5g5qdkmw4qJkHXFlVvx3U7Mq9zRtcR5IkSeuRyYbkc4F/Gj6KLcn9gHezEsEyySZJtuufNgGwdb+/ZVUV8EHgH5O8OMkTgBOA6+ke0UZVXQ6cDnwiydOTPBP4CPCl/skWAF8AlgLHJ3l8klfSPc3i6MFQPgTsnuSgJI9Jchjw1P5ckiRJWs9MdrnF2+jC6XVJLurbnkS3/GC3lTjPU4GzBvtjwfWzwD7A++iepXwc3brnbwO7V9Vdg2P2pguz36B7qsVJdM9WBronYiTZDTgWOB+4CTi8qo4b1Hw3yV7Ae4D3Aj8F9qyqS1fiXiRJkrSOSDdhO4kDu6dY7A08pm+6HDixqu5cTWObVvqlIIsWLVrErFmz1vr1tz/4hLV+TUlrx/lHvXrUQ5CkdcLixYvZbLPNADarqsXLqp3UTHKSQ4GFVfWJpn3fJA+uqiMnc15JkiRpKpjsmuT/D7hinPYf4zfUSZIkaZqbbEiew70fyzbm18BDJj8cSZIkafQmG5KvBZ45Tvsz6Z4+IUmSJE1bk326xSeAD/bPHv5m37Yr3dMo1pdv3JMkSdI6arIh+SjgQcBHgY36truAI6vqiNUxMEmSJGlUJhWS+y/6+Lsk/ww8FrgT+Gn/Vc2SJEnStDbZmWQAquo24AeraSySJEnSlDDZD+5JkiRJ6yxDsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEmNKR+Sk/wiSY2zHdv3nz1O38ebc2yZ5NQkdyS5MclRSWY0NbskuSDJkiRXJdlnLd6mJEmSppAZyy8ZuacBGwz2twX+G/jqoO0TwLsG+3eM/ZBkA+BUYAGwI/AQ4ATgd8Df9zVb9zUfB/YGdgU+meSGqpq/mu9HkiRJU9yUD8lV9evhfpJ3AD8D/mfQfEdVLZjgFLsBjwOeV1ULgQuTvBM4MslhVbUU2A+4uqoO6o+5PMmzgLcD44bkJDOBmYOmTVfy1iRJkjRFTfnlFkNJNgL+N/CpqqpB195JbkpyaZIjktx/0DcXuKQPyGPmA7OAxw9qzmwuN79vn8ihwKLBdt1K35AkSZKmpCk/k9zYE9gc+Myg7QvANcD1wBOBI4FtgL/s++cAw4DMYH/OcmpmJblfVd05zliOAI4e7G+KQVmSJGmdMN1C8uuA06rq+rGGqjpu0H9JkhuAbyR5ZFX9bE0NpKqWAEvG9pOsqUtJkiRpLZs2yy2S/BnwPOCTyyk9r399VP+6AJjd1Mwe9C2rZvEEs8iSJElah02bkAy8FriR7ikUy7Jd/3pD/3ou8IQkWwxq5gGLgcsGNbs255nXt0uSJGk9My1CcpL70IXkz1bV7wftj0zyziTbJ9kqyYvpHu92TlVd3JedQReGP5fkSUmeD7wHOLZfMgHdo98ekeR9SR6T5E3AK4Bj1tItSpIkaQqZFiGZbpnFlsCnmvalfd8ZwBXAB4CTgBeNFVTV3cALgbvpZoY/Txek3zWouRp4Ad3s8UXAQcDrfUayJEnS+mlafHCvqs4A/uSTcVV1LbDzChx/DbDHcmrOBp48ySFKkiRpHTJdZpIlSZKktcaQLEmSJDUMyZIkSVLDkCxJkiQ1DMmSJElSw5AsSZIkNQzJkiRJUsOQLEmSJDUMyZIkSVLDkCxJkiQ1DMmSJElSw5AsSZIkNQzJkiRJUsOQLEmSJDUMyZIkSVLDkCxJkiQ1DMmSJElSw5AsSZIkNQzJkiRJUsOQLEmSJDUMyZIkSVLDkCxJkiQ1DMmSJElSw5AsSZIkNQzJkiRJUsOQLEmSJDUMyZIkSVLDkCxJkiQ1DMmSJElSw5AsSZIkNQzJkiRJUsOQLEmSJDUMyZIkSVLDkCxJkiQ1pnRITnJYkmq2Kwb9901ybJLfJLktyUlJZjfn2DLJqUnuSHJjkqOSzGhqdklyQZIlSa5Kss9aukVJkiRNQVM6JPd+DDxksD1r0HcM8CLg5cDOwEOBr411JtkAOBXYCNgReA2wD3D4oGbrvuYsYDvgg8Ankzx/Td2QJEmSprYZyy8Zud9X1YK2MclmwOuAvarqm33ba4HLk+xQVd8DdgMeBzyvqhYCFyZ5J3BkksOqaimwH3B1VR3Un/ryJM8C3g7MX+N3J0mSpClnOswkPzrJ9Ul+nuTEJFv27dsDGwJnjhVW1RXAL4G5fdNc4JI+II+ZD8wCHj+oOZN7mz84x7iSzEwya2wDNp3EvUmSJGkKmuoh+Ty65RG7A28Etga+lWRTYA6wtKpuaY5Z2PfRvy4cp58VqJmV5H7LGNuhwKLBdt0K3I8kSZKmgSm93KKqThvsXpzkPOAa4BXAnaMZ1R8cARw92N8Ug7IkSdI6YarPJN9LP2v8E+BRwAJgoySbN2Wz+z7619nj9LMCNYurasIgXlVLqmrx2AbculI3I0mSpClrWoXkJJsAjwRuAM4HfgfsOujfBtgSOLdvOhd4QpItBqeZBywGLhvU7Mq9zRucQ5IkSeuZKR2Sk7w/yc5JtkqyI/D/A3cDX6yqRcDxwNFJnpNke+DTwLn9ky0AzqALw59L8qT+sW7vAY6tqiV9zceBRyR5X5LHJHkT3XKOY9benUqSJGkqmdJrkoGHAV8EHgT8Gvg2sENV/brvfztwD3ASMJPuqRRvGju4qu5O8kLgY3Qzw7cDnwXeNai5OskL6ELxW+nWFb++qnz8myRJ0npqSofkqnrVcvrvAvbvt4lqrgH2WM55zgaePIkhSpIkaR00pZdbSJIkSaNgSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqWFIliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZIkqTGlQ3KSQ5P8IMmtSW5McnKSbZqas5NUs328qdkyyalJ7ujPc1SSGU3NLkkuSLIkyVVJ9lkLtyhJkqQpaEqHZGBn4FhgB2AesCFwRpKNm7pPAA8ZbIeMdSTZADgV2AjYEXgNsA9w+KBm677mLGA74IPAJ5M8f03clCRJkqa2GcsvGZ2q2n2438/u3ghsD5wz6LqjqhZMcJrdgMcBz6uqhcCFSd4JHJnksKpaCuwHXF1VB/XHXJ7kWcDbgfnjnTTJTGDmoGnTlbo5SZIkTVlTfSa5tVn/enPTvneSm5JcmuSIJPcf9M0FLukD8pj5wCzg8YOaM5tzzu/bJ3IosGiwXbfityFJkqSpbErPJA8luQ/dMojvVNWlg64vANcA1wNPBI4EtgH+su+fAwwDMoP9OcupmZXkflV15zhDOgI4erC/KQZlSZKkdcK0Ccl0a5O3BZ41bKyq4wa7lyS5AfhGkkdW1c/W1GCqagmwZGw/yZq6lCRJktayabHcIslHgBcCz6mq5c3Wnte/Pqp/XQDMbmpmD/qWVbN4gllkSZIkrcOmdEhO5yPAS4HnVtXVK3DYdv3rDf3rucATkmwxqJkHLAYuG9Ts2pxnXt8uSZKk9cxUX25xLLAX8BLg1iRja4gXVdWdSR7Z938d+A3dmuRjgHOq6uK+9gy6MPy5JIfQrT9+D3Bsv2QC4OPAAUneB3wKeC7wCuAFa/oGJUmSNPVM6Zlk4I10T7Q4m25meGx7Zd+/FHgeXRC+AvgAcBLworETVNXddEs17qabGf48cALwrkHN1XSBeB5wEXAQ8PqqGvfxb5IkSVq3TemZ5Kpa5qfhqupaui8cWd55rgH2WE7N2cCTV2Z8kiRJWjdN9ZlkSZIkaa0zJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJDUOyJEmS1DAkSzbrWr0AAA1DSURBVJIkSQ1DsiRJktQwJEuSJEkNQ7IkSZLUMCRLkiRJjRmjHsBUk2R/4GBgDnAR8Oaq+v5oRyVJ659fHv6EUQ9B0hqy5bsuGfUQlsuZ5IEkrwSOBv4JeApdSJ6fZIuRDkySJElrlSH53g4EPlFVn66qy4D9gDuAfUc7LEmSJK1NLrfoJdkI2B44Yqytqu5JciYwd5z6mcDMQdOmAIsXL17DIx3f3UvuHMl1Ja15o3pfGbVb77p71EOQtIaM6n1tZa6bqlqDQ5k+kjwU+BWwY1WdO2h/H7BzVT2jqT8MePdaHaQkSZJWh4dV1a+WVeBM8uQdQbd+eeiBwM0jGIvWL5sC1wEPA24d8VgkaXXwfU1r06bA9csrMiT/0U3A3cDspn02sKAtrqolwJKmef38najWqiRjP95aVf43J2na831Na9kK/TfmB/d6VbUUOB/YdawtyX36/XMnOk6SJEnrHmeS7+1o4LNJfgh8H3gbsDHw6ZGOSpIkSWuVIXmgqr6c5MHA4XRfJnIhsHtVLRztyKR7WUL3LO92uY8kTVe+r2nK8ekWkiRJUsM1yZIkSVLDkCxJkiQ1DMmSJElSw5AsaYUlOTvJB0c9DknrrySHJblwsP+ZJCev4jl3SVJJNl/1EWpdYUiWVkGSuUnuTnLqqMciSasqyZwkH07y8yRLklyb5JQkuy7/aGndYkiWVs3rgA8DOyV56Jq+WJKN1vQ1JK2fkmxF96VazwUOBp4A7A6cBRw7soFJI2JIliYpySbAK4GPAacC+wz6xn5194IkFye5K8n3kmw7qNknyS1J9kzy075mfpKHD2oOS3JhktcnuRq4q2/fMsl/JLktyeIkX0kyu+/78/7aj2nG+/YkPxvsb5vktP4cC5N8Lsn/GvRvnOSEvv+GJAet7j9DSVPKR4ECnl5VJ1XVT6rqx1V1NLADQJIDk1yS5PZ+lvmj/Xshff/Y+9rzk1zev3+cnuQhwwsl2TfJj/vZ6huSfGTQt3mSTyb5df/+9s0kT1rRm0hynySHJrk6yZ1JLkryV03NHkl+0vefBWw1qT8xrdMMydLkvQK4oqquBD4P7JskTc1RwEHA04BfA6ck2XDQf3/gH4BXA88ENge+1JzjUcDLgL8Etuu/Lv0/gAcCOwPzgEcAXwaoqp8APwT2bs6zN/AF6P4SAr4J/Ah4Kt1s0WzgK83YdwZeAuwG7AI8Zbl/KpKmnSQPpHsfOLaqbm/7q+qW/sd7gLcAjwdeQzfr/L6m/P7A3wL/B9gJ2BJ4/+Bab6SbmT6Obrb6xcBVg+O/CmwB/AWwPXAB8I1+jCviULr31P36cR4DfD7Jzv31Hw58DTgF2A74JPAvK3hurU+qys3NbRIb8B3grf3PM+hC8C79/i50MzKvHNQ/ELgDeEW/v09f84xBzWP440wOwGHAUuDBg5p5wO+Bhw/aHtcf97R+/23AVYP+P+/7H9Pv/yMwv7mfh/U1fw5sQvfNVy8fZ/wfHPWfvZub2+rdgKf3//9/6Uoe91fATYP9sfe1Rw7a3gQsGOz/CnjPBOd7FrAImNm0XwW8of/5MODCQd9ngJP7n2cCtwNzm+M/CXyh//m9wI+b/n/px735qP+3cJs6mzPJ0iQk2YbuL5UvAlTV7+lmcl/XlJ479kNV3QxcCTx20P974AeDmiuAW5qaa6rq14P9xwLXVtW1g+Mua477ErBVkh36/b2BC/rzAzwJeE7/q9DbktwGjPU9st82As4bZ/yS1j3tb8HGL0qel+QbSX6V5Fbgc8CDktx/UHZHVf1ssH8D3cwwSbYAHgp8Y4JLPInuH+m/ad6ftqZ7X1qeR9HNZP93c/yrB8c/lsF7W+9cpMaMUQ9AmqZeR/f/n+sHKywCLElywGq+1p/86nN5qmpBkm8CewHf618/NijZhO5XjX83zuE30P1FI2n98VP63zZNVNB/sO+/6N5L/gG4mW7m93i6f1Tf0Zf+rjm0+GMIv3M549iE7j1ol3H6bhmnbbzjAV5AN2M9tGQFjpf+wJlkaSUlmUE3K3EQ3Xq2se1JwPXAXw/Kdxgc9wC6pQyXD/pn0K0JHqvZhm5d8rCmdTnw8OYDfo/rj7tsUHci8Mokc+nWLA/XOl9At1bvF1V1VbPdDvyM7i+6Z4wzfknrmP43RfOB/ZNs3Pb3n2PYni43HFRV36vu8w8r9VSfqroV+AUw0SPlLgDmAL8f573pphW4xGV0YXjLcY4f++3b5XS/CRzaAalhSJZW3guBBwDHV9Wlww04iXsvuXhXkl37p1p8BrgJGD70/nfAh5M8I8n2fc33qur7y7j+mcAlwIlJnpLk6cAJwP9U1Q8HdV8DNqWb9Tmrqq4f9B1Lt8b4i0meluSR/afRP51kg6q6jW526Kgkzx2M/56V+HOSNL3sD2wAfD/Jy5I8Osljk7yFbjnCVcCGwJuTPCLJ/6H7cNzKOgw4KMlb+ms8Jcmb+74z+2udnGS3JFsl2THJ/03y1AnP2OtD+PuBY5K8pn9ve0qSNyd5TV/2ceDRSY5Ksk2SvRg8nUgaY0iWVt7rgDOratE4fSfRzQw/sd9/B/AhumePzgFeVFVLB/V3AEfSPXXiO8BtdI+Vm1BVFd0TJ34LnEP3l8rP2+P6vyxOoZvhPrHpu57uaRobAGfQhe4P0v06cywIHwx8qz/HmcC3+/uQtA6qqp/TPcHmLOADwKXAf9PN+r6xqi4CDqRbpnUp3WcdDp3EdT5L9+HiNwE/plvC8ei+r4A96N7bPg38hO63YH8GLFzBS7wT+Od+bJcDp9Mtv7i6v8Yv6Z4YtCdwEV3Q//uVvQ+t+9L99yhpdUqyC91fNA+oPz46qa3Zh+5JEX4NqiRJU4wzyZIkSVLDkCxJkiQ1XG4hSZIkNZxJliRJkhqGZEmSJKlhSJYkSZIahmRJkiSpYUiWJEmSGoZkSZrmkpyd5IOjHockrUsMyZKkVZLksCQXTuK4fZKM+42UkjRqhmRJmgbSmbGGzr1BEv8+kKQB3xQlaUSSzEzyr0luTHJXkm8neVrft0uSSvIXSc4HlgDPSrJxkhOS3JbkhiQHTXDe9yf5VZLbk5yXZJdB/z5Jbkny4iSX9efecjlj3SXJ9/vz3ZLkO0n+LMk+wLuBJ/Xjrb6NJAcmuaQ/5tokH02yydj5gE8Dmw2OO6zvqyR7Nte/ZXDejZJ8pL//u5Jck+TQlf9fQJImtkZmJSRJK+R9wMuA1wDXAIcA85M8alDzL8DfAj8HfgscBewMvAS4EXgv8BRguNzhI8DjgFcB1wMvBU5P8oSq+mlfc3/g74DXA7/pzzWufgb7ZOATwF8DGwFPBwr4MrAtsDvwvP6QRf3rPcBbgKuBRwAf7e/5TcB3gbcBhwPb9PW3TfxHdS9vAV4MvAL4JfDwfpOk1caQLEkjkGRj4I3APlV1Wt/2N8A84HXAD/rSd1XVf/f9m/R9/7uqvtG3vQa4bnDeLYHXAltW1fV98/uT7N63/33ftiHwpqq6aAWGOwvYDPivqvpZ33b54Jq3Ab+vqgXDg6pq+GHCXyT5R+Dj/XWXJlnUld37uBWwJfBT4NtVVXT/wJCk1crlFpI0Go+kC6rfGWuoqt8B3wceO6j7YXPMRsB5g2NuBq4c1DwB2AD4Sb8k47Y+xO7cHz9mKXDxigy0v8Zn6Ga5T0ny1iQPWd5xSZ6X5Bv9so9bgc8BD0py/xW57jJ8BtgOuLJfrrLbKp5Pkv6EIVmSprbbV7J+E+BuYHu6IDm2PRZ466Duzn4WdoVU1WuBuXTLJF5JF8J3mKg+yVbAf9EF8Zf149m/795oeZcD0rRtOBjLBcDWwDuB+wFfSfLvK3grkrRCDMmSNBo/o5vNfeZYQ5INgacBly3jmN8Bzxgc8wDgzwc1P6KbSd6iqq5qtpVd1nAvVfWjqjqiqnYELgX26ruW9tcc2p7u75iDqup7VfUT4KFNzXjHAfwa+MNMdZJH062hHo5lcVV9uar+hi60vyzJAyd5a5L0J1yTLEkjUFW3J/kYcFSSm+k+gHYIXRg8HnjSOMfcluT4/pixD9v9X7oPyI3V/CTJicAJ/ZMvfgQ8GNgVuLiqTl3ZsSbZGngD8J90HwTcBng0cEJf8gtg6yTb0a2PvhW4im72981JTqH7x8B+zal/AWySZFfgIuCOqroD+CZwQJJz6UL0kXT/OBgbz4HADf293QO8HFgA+MxlSauNM8mSNDrvAE6iW6t7AfAo4PlV9dtlHHMw8C3gFOBM4NvA+U3Na+kC7Afo1iufTDdD/ctJjvMO4DH9WH8CHAccC/xb338ScDpwFt0s8F/3Hwg8kO4JGpcCewP3ekxbVX2X7oN8X+6PO6TvOgi4tr/PLwDv78cw5ta+9od0H3DcCtijqu5BklaTrMSSNEmSJGm94EyyJEmS1HBNsiRp7FnHE/mLqvrWWhuMJE0BhmRJEnSPiZvIr9baKCRpinBNsiRJktRwTbIkSZLUMCRLkiRJDUOyJEmS1DAkS5IkSQ1DsiRJktQwJEuSJEkNQ7IkSZLU+H+N87eSFu30kgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def boxplots(num_list):\n",
        "  for elem in num_list:\n",
        "    plt.rcParams.update({'figure.figsize':(8,5),'figure.dpi':100})\n",
        "    sns.boxplot(y = customer_data[elem])\n",
        "    plt.show()\n",
        "\n",
        "boxplots(numerical_variables)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nRafPLpAGeHH",
        "outputId": "0315eb4e-72f1-4063-dcae-794ec7a1d158"
      },
      "execution_count": 473,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V24h4B3Ah1aN1dwZm1pfm0eBsbkT8HnA58ASwEPhj4HDgwY7bjgAOAQ4EdgMeBS6MiIGmckiSJGnNUDpz+0ngwMz8AFXxHHY5Vdltyt8Dd2bm/pn508y8NTO/n5m/gWrWFjgU+ExmnpeZNwDvAzYD3t5gDkmSJK0BSsvt9sCPRhlfDmxQHudZ3gosiYj/FxH3RsS1EdG5Bdk2wCbAxcMDmbmcageH+WN9aETMjIi5wy9gToOZJUmS1Cel5fZu4EWjjL8W+G15nGf5Q+Ag4BbgzcA/AV+MiH3r65vUP+8Z8b57Oq6NZhFVER9+LW0qsCRJkvqntNz+M3ByROwGJLBZRLwX+DxVAW3KOsA1mXlkZl6bmafXv/vAHj/3OKr1wcOvLXr8PEmSJE0BpVuBfY6qeF4CzKZaojAEfD4zT2koG8BdwM0jxn7O0w+KuLv+uXF9Lx3n1431oZk5RJUXgGrpriRJktZ0pQ9xyMz8LNXDG3YAXgVslJmfajIc1RfUth8xth1we318K1XB3XP4Yr2GdjfgioazSJIkaYor3QrsjIiYk5mPZ+bN9U4Gj0TEehFxRoP5TgReFRFHRsSLImIf4IPAaVCVbOAk4JMR8daI+BPg68Ay4DsN5pAkSdIaoHTN7b7ArFHGZ1FtxdWIzPxPYG/gPcBNwKeAQzPzrI7bjgdOAU4H/hNYH1iQmYNN5ZAkSdKaoas1t/Vf+Uf9mhMRnQVyGvBnwL3NxYPM/Hfg35/jegJH1S9JkiStxbr9QtlDVLsjJPCrUa4n8OleQ0mSJEklui23u1PN2l5KtWPBAx3XHgduz8xlDWWTJEmSutJVuc3M/wCIiG2oHou7akJSSZIkSQWK9rnNzNsBImI2sBUwY8T1G3qPJkmSJHWnqNxGxEbAV4CFY9wyrTiRJEmSVKh0K7CTgA2oHpawElhAtT3YLcBbm4kmSZIkdaf08bt7AG/LzCURsYrqi2QXRcQKYBHwvcYSSpIkSeNUOnO7Hk/vZ/sgsFF9fCOwc6+hJEmSpBKl5faXwPb18fXAhyJic+BA4K4mgkmSJEndKl2WcDKwaX18NHAB8F6qvW736z2WJEmS1L3SrcDO7Di+OiK2Bl4M3JGZ9zUVTpIkSepG6cztM2TmY8A1TXyWJEmSVGrc5TYiThjvvZl5WFkcSZIkqVw3M7c7jfO+LAkiSZIk9Wrc5TYzd5/IIJIkSVKvSrcCAyAiXhQRb46IWfV5NBNLkiRJ6l5RuY2IF0TEJcCvgPN5eluwL0fEF5oKJ0mSJHWjdOb2ROAJYCvgsY7xbwILeg0lSZIklSjdCmwv4M2ZuXTESoRbgK17TiVJkiQVKJ25XY9nztgO2xAYKo8jSZIklSsttz8G3tdxnhGxDnAE8IOeU0mSJEkFSpclHAFcEhGvAGYAxwMvpZq5fU1D2SRJkqSuFM3cZuZNwHbAZcB5VMsUzgF2yszfNBdPkiRJGr+uZ24jYjpwAXBgZn62+UiSJElSma5nbjPzCeBlE5BFkiRJ6knpF8rOBA5oMogkSZLUq9IvlK0LvD8i3ghcDTzaeTEzD+s1mCRJktSt0nK7A3BNfbzdiGtZHkeSJEkqV1RuM3P38dwXEVsAyzJzVcnvkSRJkrpRuuZ2vG4G/mCCf4ckSZIETHy5jQn+fEmSJGm1iS63kiRJ0qSx3EqSJKk1SndL0Bous2NTi6ee6F8QSZI0fh3/zX7Gf8u12kSXW/+/PkUNDQ2tPp5z/Tf6mESSJJUYGhpi9uzZ/Y4x5fiFMkmSJLVGTzO3EfEi4I+AH2XmyoiIfOYc+R8Dy3r5HZoYM2fOXH388MvfDdOm9zGNJEkal6eeWP03rp3/LdfTisptRLwA+CawB9XSg22B3wJfjogHM/NwgMy8s6mgalZEx6T6tOmWW0mS1jDP+G+5VitdlnAi8CSwFfBYx/g3gQW9hpIkSZJKlC5L2At4c2YuHfG/Gm4Btu45lSRJklSgdOZ2PZ45YztsQ2BolHFJkiRpwpWW2x8D7+s4z4hYBzgC+EHPqSRJkqQCpcsSjgAuiYhXADOA44GXUs3cvqahbJIkSVJXimZuM/MmYDvgMuA8qmUK5wA7ZeZvmosnSZIkjV/pVmC7Z+YPgM+Ocu3gzDyt52SSJElSl0rX3J4TEbuMHIyIjwLH9RZJkiRJKlNabv8OWBwRLx4eiIjDgWOAtzQRTJIkSepW0bKEzPxSRGwIXBwRrwXeBRwJ/FlmXt5kQEmSJGm8SndLIDOPrx/DuwSYRvVQhysbSyZJkiR1adzlNiIOGWX4d1QPc/gRsGtE7AqQmV9sJp4kSZI0ft3M3H5sjPGnqPa2Hd7fNgHLrSRJkibduMttZm4zkUEkSZKkXpXuliBJkiRNOd2suT0B+FRmPlofjykzD+s5mSRJktSlbtbc7gRM7zgeS5bHkSRJksp1s+Z299GOJUmSpKmi5zW3EbFlRGzZRBhJkiSpF0XlNiLWjYhjI2I5cBtwW0Qsj4jPRMT053m7JEmSNCFKZ25PAT4IHEG1/nan+vgAJnCP24j4eERkRJzUMTYQEadFxP0R8UhEfDsiNp6oDJIkSZq6Sh+/uw/w7sxc3DF2Q0TcCZwNHNRzshEi4pXAh4AbRlw6EXgL8JfAcuBU4ByefqiEJEmS1hKlM7dDVMsRRroVeLw4zRgiYn3gLOADwIMd4/OoZosPy8xLM/NqYH/g1RHxqqZzSJIkaWorLbenAp+KiJnDA/XxJ+prTTsN+F5mXjxifBeq7clWj2fmL4A7gPljfVhEzIyIucMvYM4EZJYkSdIk6+YhDueMGHojsDQirq/PXw7MAC5pKNvw7303sDPwylEubwI8npkPjRi/p742lkXAp5tJKEmSpKmimzW3y0ecf3vE+Z09ZnmWeouxk4E3ZeZggx99HND5lLU5wNIGP1+SJEl90M1DHPbv9sMj4jXAkswc6va9tV2AFwLXRMTw2DTg9RHxYeDNwIyI2GDE7O3GwN1jfWidZ3Wmjs+WJEnSGqznhzg8j8XA5j28/xLgT4AdO15LqL5cNnz8BLDn8BsiYntgK+CKHn6vJEmS1kClW4GNV09Topn5MHDTMz4w4lHg/sy8qT7/MnBCRDwArKDag/eKzLyyl98tSZKkNc9El9vJ8DFgFdUa4JnAhcDf9jWRJEmS+mKNK7eZ+YYR54PAwfVLkiRJa7GJXnMrSZIkTZqJLrc5wZ8vSZIkrTbR5dY9tiRJkjRpitbcRsQsIDLzsfp8a2Bv4ObM/P7wfZnpY20lSZI0aUpnbs8D3gcQERsAVwGHA+dFxEENZZMkSZK6UlpudwZ+XB//d+AeYGuqwntIA7kkSZKkrpWW29nAw/XxXsA5mbkKuJKq5EqSJEmTrrTc/hp4e0RsCbwZGF5n+0Kqp4RJkiRJk6603B4DfB64DbgqM6+ox/cCrm0glyRJktS1ot0SMvNbEXEZsClwfcelS4BzmwgmSZIkdavrchsR04GVwI6Z+YxZ2sz8aVPBJEmSpG51vSwhM58A7gCmNR9HkiRJKle65vazwD9GxIZNhpEkSZJ6UbTmFvgw8CJgWUTcDjzaeTEzd+41mCRJktSt0nL7nUZTSJIkSQ0o3S3h6KaDSJIkSb0qXXMrSZIkTTlFM7cRMQ34GPBOYCtgRuf1zPSLZpIkSZp0pTO3nwYOA74JzANOAM4BVgH/0EgySZIkqUul5fa9wAcy8wvAk8DZmfk3VI/lfVVT4SRJkqRulJbbTYAb6+NHqGZvAf4deEuvoSRJkqQSpeV2KbBpffwbYK/6+JXAUK+hJEmSpBKl5fZcYM/6+BTg2Ii4Bfg6cEYTwSRJkqRule5z+/GO429GxB3AfOCWzPxuU+EkSZKkbpQ+oewZMvMK4IomPkuSJEkqVfwQh4j464i4PCKWRcTW9dihEfG25uJJkiRJ41dUbiPiIKq9bc8HNgCm1ZceAg5tJpokSZLUndKZ249Q7XP7WeCpjvElwJ/0nEqSJEkqUFputwGuHWV8CFivPI4kSZJUrrTc3grsOMr4AuDn5XEkSZKkcqW7JZwAnBYRA0AAu0bEe4BFwN80FU6SJEnqRuk+t1+KiJXAZ4DZwL8Ay4CPZuY3GswnSZIkjVvxPreZeRZwVkTMBtbPzHubiyVJkiR1r3QrsKOH97bNzMcstpIkSZoKSr9Q9jbgNxFxSUTsExEzmwwlSZIklSgqt5m5I/BK4GfAycDdEfFPEfHKJsNJkiRJ3Sh+/G5mXpuZhwCbAQcAWwCXR8QNEfHRiJjXVEhJkiRpPIrLbYcApgMz6uMHgQ8Dd0bEuxr4fEmSJGlcisttROwSEacCdwEnUj2x7CWZ+aeZuS3wCeCLzcSUJEmSnl/pbgk3AldSPYb3AGDLzPx4Zv6647azgY16jyhJkiSNT+k+t/8KnJGZvxvrhsy8j2aWPUiSJEnjUrpbwrHPVWyHRcSKiPjDkt8hSZIkdWuiZ1Zjgj9fkiRJWs1lA5IkSWoNy60kSZJaw3IrSZKk1pjocpsT/PmSJEnSaqVbgY2XXyhbA8SqJ/1fIVKbZMKqJ6vjddaF8F/FUlvE8J9tjWmiy+1C4Hm3DFN/rX/d2f2OIEmS1IiulyVExJsi4uiI2KM+f31ELI6ISyNi/857M/OyzBxqKqwkSZL0XLqauY2IvwK+AtwAHBYRHwFOBL5FVZT/d0Q8nJnfajypGjUwMMDixYv7HUPSBBgcHGTvvfcG4Nxzz2VgYKDPiSRNBP9sj67bZQmHA4dn5hcjYk/gu8AnMvNEgIi4GTiUquxqCosIZs2a1e8YkibYwMCAf9YlrVW6XZawLVWhJTMvoSrHl3Rc/x7w4maiSZIkSd3pttw+AczoOB8CHhlx7hSBJEmS+qLbcvtrnjkzuzlwa8f5HwFLew0lSZIkleh2ze0/Ag8On2TmihHXXwH8a6+hJEmSpBJdldvMPPd5rn+u8zwiXgMscTswSZIkTYaJfvzuYqqlC5IkSdKEm+hy6zMfJUmSNGkmutz2LCIWRcR/RsTDEXFvRHwnIrYfcc9ARJwWEfdHxCMR8e2I2LhfmSVJktQfU77cAn8KnAa8CngTMB34fkSs13HPicBfAH9Z378ZcM4k55QkSVKfdbtbwqTLzAWd5xGxH3AvsAvwo4iYBxwA7JOZl9b37A/8PCJelZlXTnJkSZIk9clEz9zmBHzmvPrnA/XPXahmcy9e/UszfwHcAcwf7QMiYmZEzB1+AXMmIKckSZIm2Rr1hbKIWAc4Cbg8M2+qhzcBHs/Mh0bcfk99bTSLgOUdLx88IUmS1AJF5TYiLo2IDUYZnxsRlw6fZ+aczPxtLwFHOA3YAXh3j59zHNUM8PBrix4/T5IkSVNA6ZrbNwAzRhkfAF5XnOY5RMSpwJ8Dr8/MzpnWu4EZEbHBiNnbjetrz1I/VGL1gyUi3LFMkiSpDboqtxHxso7TP46Izr/2nwYsAH7XRLCO3xnAKcDewBsy89YRt1wNPAHsCXy7fs/2wFbAFU1mkSRJ0tTW7cztdVRfEkvg0lGurwQ+0muoEU4D9gHeBjzcUaiXZ+bKzFweEV8GToiIB4AVVGX4CndKkCRJWrt0W263ofqS2G+BXYH/33HtceDezHyqoWzDDqp//nDE+P7AV+vjjwGrqGZuZwIXAn/bcA5JkiRNcV2V28y8vT6ctIc/ZObzLojNzEHg4PolSZKktVTpbgn7RsRbOs6Pj4iHIuInEbF1c/EkSZKk8SudgT2San0tETEf+DBwBHAf1aNwJUmSpElXuhXYlsCv6+O3A9/KzNMj4nKevTZWkiRJmhSlM7ePAC+oj/cCLqqPB4FZvYaSJEmSSpTO3F4EfCkirgW2A86vx18K3NZALkmSJKlrpTO3B1M9IGEj4B2ZeX89vgtwdhPBJEmSpG4VzdzWj7n98Cjjn+45kSRJklSodFkCABExm+oxtzM6xzPzhl4+V5IkSSpRVG4jYiOqp4MtGOOWaaWBJEmSpFKla25PAuYBu1Htd7sA2Be4BXhrM9EkSZKk7pQuS9gDeFtmLomIVcDtmXlRRKwAFgHfayyhJEmSNE6lM7frAffWxw9S7ZoAcCOwc6+hJEmSpBKl5faXwPb18fXAhyJic+BA4K4mgmSJFp4AAARZSURBVEmSJEndKl2WcDKwaX18NHAB8FfA41RrbyVJkqRJV7rP7Zkdp9cAWwMvBu7IzPuaCCZJkiR1q3RZAhFxQETcBAxSrbv9OvD2poJJkiRJ3Srd5/YY4DDgFKrH8ALMB06MiK0y86iG8kmSJEnjVrrm9iDgA5l5dsfYv0XEDVSF13IrSZKkSVe6LGE6sGSU8avp8ZG+kiRJUqnScvt/qWZvR/ogcFZ5HEmSJKlcL7OsB0TEXsCV9fluwFbA1yPihOGbMvOwHn6HJEmSNG6l5XYHqi3AAP6o/nlf/dqh474s/HxJkiSpa6X73O7edBBJkiSpV8X73EqSJElTjeVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmtYbiVJktQalltJkiS1huVWkiRJrWG5lSRJUmu0ptxGxMERcVtEDEbEVRGxa78zSZIkaXK1otxGxLuAE4CjgZ2B64ELI+KFfQ0mSZKkSbVuvwM05DDgnzPzKwARcSDwFuD9wOf6GUyaCjKTwcHBfsfQJOn8Z+0/97XLwMAAEdHvGFJfrfHlNiJmALsAxw2PZeaqiLgYmD/Ge2YCMzuG5kxoSKnPBgcHWbhwYb9jqA/23nvvfkfQJFq8eDGzZs3qdwypr9qwLOH3gWnAPSPG7wE2GeM9i4DlHa+lE5ZOkiRJk2aNn7ktdBzVGt1hc7DgqsUGBgZYvHhxv2NokmQmQ0NDAMycOdO/pl6LDAwM9DuC1HdtKLf3AU8BG48Y3xi4e7Q3ZOYQMDR87r/41XYR4V9VrmVmz57d7wiS1Bdr/LKEzHwcuBrYc3gsItapz6/oVy5JkiRNvjbM3EK1xOBrEbEE+ClwKLAe8JW+ppIkSdKkakW5zcxvRsRGwDFUXyK7DliQmSO/ZCZJkqQWa0W5BcjMU4FT+51DkiRJ/bPGr7mVJEmShlluJUmS1BqWW0mSJLWG5VaSJEmtYbmVJElSa1huJUmS1BqWW0mSJLWG5VaSJEmtYbmVJElSa7TmCWVNWLFiRb8jSJIkaRTj7WmRmRMcZeqLiM2Bpf3OIUmSpOe1RWb+bqyLllsgIgLYDHi431kkqSFzqP5H+xb47zZJ7TEHWJbPUWAtt5LUQhExF1gOzMtM11xJWmv4hTJJkiS1huVWkiRJrWG5laR2GgKOrn9K0lrDNbeSJElqDWduJUmS1BqWW0mSJLWG5VaSJEmtYbmVJElSa1huJUmS1BqWW0mSJLWG5VaSJEmtYbmVJElSa/wXhDXOiA/JBvIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AgNXOuD43ySePcfyT47FktJzgGau8PgAAPM5qi+udSV5XVacsH6iqbUleNx5LkmcmObi2eAAAMLLapQKvSvLrSe6tqo+Ojz03yclJvn28/5VJfmFt8QAAYGRVxbW19oGqenaS787oiwiS5NeS/PfW2oPjc/7r+kTk76u1lkOH3MwBNqPJv21/57B5zc7Opqqe/MQtplprT37W8V5c9ZwkezL62tejWmu/vsZc66aq5pIsLCwsZG5ubug4U/HII4/k4osvHjoGALBK+/bty44dO4aOMTWLi4uZn59PkvnW2uLxzlvtFxB8ZZJbM1oe0DK6l+tkAz55NdcFAIDjWe0a159Lck+S88eP5yT50rj91Ybz0Nd/V9pJq/5mX2CjaS157Mjo+UkzibcSYdOox47k6R9519AxNrTVNprzknxba+1vquqxJF9orb2vqq5O8vNJvmHdErIm7aSZ5ORtQ8cA1tUpT34K0J3VL97cOlZ7O6yTkzw4fv43SU4fP/9kkrPWGgoAAFZa7YzrxzL6mtd7knwoyVVV9WhGX/v6F+uUDQAAjlptcb0mydPGz9+Y5DeT/H6SB5L8q3XIBQAAj7Pa+7jePvH8E0n+YVV9aZK/bWu5vxYAABzHun3cvLX22fW6FgAArLTaD2cBAMBUKa4AAHRh0OJaVT9RVW3FdufE+GxV3VBVD1TVQ1V1S1XtHjIzAADD2Agzrn+S5BkT2zdPjF2X5MVJXprkhRndL/bd0w4IAMDwNsJ3gR5prd2/8mBVzSe5MsllrbX3jo9dkeTjVXVua+2DU84JAMCANsKM65lVdV9V/UVVvbOq9oyPn51kW5L9yye21u5MciCjr5w9pqraXlVzy1uSXScyPAAA0zF0cf1Qku9PclGSvUmeneT3q2pXktOSPNpa+9yK1xwcjx3P1UkWJrZ71zkzAAADGHSpQGtt38TuR6vqQ0k+meQ7kzyyystem+TNE/u7orwCAHRv6BnXxxnPrv5Zkq9Kcn+SU6rq1BWn7R6PHe8aS621xeUtyYMnLDAAAFOzoYprVT09yT9I8ukkH05yOMn5E+NnJdmT5I5BAgIAMJhBlwpU1X9M8hsZLQ84PclPJvlCkne11haq6u1J3lxVn02ymOT6JHe4owAAwNYz9O2wzkjyriRfluSvk7wvybmttb8ej782yWNJbkmyPcntSV45QE4AAAY29IezLn2S8UNJXjXeAADYwjbUGlcAADgexRUAgC4orgAAdEFxBQCgC4orAABdUFwBAOiC4goAQBcUVwAAuqC4AgDQBcUVAIAuKK4AAHRBcQUAoAuKKwAAXVBcAQDoguIKAEAXFFcAALqguAIA0AXFFQCALiiuAAB0QXEFAKALM0MHYP211r6484XDwwUBAJ66iX+zH/dvOUcprpvQ0tLS0ee7/t/NAyYBAFZjaWkpO3fuHDrGhmOpAAAAXTDjuglt37796PMHv+7S5ORtA6YBAJ6SLxw++k7p5L/lfJHiuglV1Rd3Tt6muAJAZx73bzlHWSoAAEAXFFcAALqguAIA0AXFFQCALiiuAAB0QXEFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC5smOJaVa+rqlZVb5k4NltVN1TVA1X1UFXdUlW7h8wJAMAwNkRxrarnJ3l5ko+uGLouyYuTvDTJC5OcnuTd000HAMBGMHhxraqnJ3lnkh9I8rcTx+eTXJnkh1tr722tfTjJFUm+qarOHSQsAACDGby4JrkhyW+11vavOH52km1Jjh5vrd2Z5ECS8453saraXlVzy1uSXScgMwAAUzYz5A+vqkuTfGOS5x9j+LQkj7bWPrfi+MHx2PFcneTH1ychAAAbxWAzrlX1rCQ/l+S7W2uH1vHS1yaZn9jOWMdrAwAwkCFnXM9O8uVJ/rCqlo+dnORbqupfJ7kwySlVdeqKWdfdSe4/3kVba0tJlpb3J64NAEDHhiyuv5PkuSuO/VKSO5P8+ySfSnI4yflJbkmSqjoryZ4kd0wvJgAAG8FgxbW19mCSj00eq6rPJ3mgtfax8f7bk7y5qj6bZDHJ9UnuaK19cNp5AQAY1qAfznoKXpvksYxmXLcnuT3JKwdNBADAIDZUcW2tfeuK/UNJXjXeAADYwjbCfVwBAOBJKa4AAHRBcQUAoAuKKwAAXVBcAQDoguIKAEAXFFcAALqguAIA0AXFFQCALiiuAAB0QXEFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC4orgAAdEFxBQCgC4orAABdmBk6ACdWPXYkbegQwPppLXnsyOj5STNJ1bB5gHVTy3/bHJfiusk9/SPvGjoCAMC6sFQAAIAumHHdhGZnZ7Nv376hYwAnwKFDh3LJJZckSW699dbMzs4OnAg4EfxtH5viuglVVXbs2DF0DOAEm52d9bcObCmWCgAA0AXFFQCALiiuAAB0QXEFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC4orgAAdEFxBQCgC4orAABdGLS4VtXeqvpoVS2Otzuq6uKJ8dmquqGqHqiqh6rqlqraPWRmAACGMfSM671JXpfk7CTPS/LeJLdV1deOx69L8uIkL03ywiSnJ3n3ADkBABjYzJA/vLX2GysOvb6q9iY5t6ruTXJlkstaa+9Nkqq6IsnHq+rc1toHpxwXAIABDT3jelRVnVxVlyZ5WpI7MpqF3ZZk//I5rbU7kxxIct4TXGd7Vc0tb0l2ndjkAABMw+DFtaqeW1UPJVlKclOSS1prf5rktCSPttY+t+IlB8djx3N1koWJ7d71Tw0AwLQNXlyT3JXk65Ock+TGJL9SVc9Zw/WuTTI/sZ2x5oQAAAxu0DWuSdJaezTJJ8a7H66q5yf5oSS/muSUqjp1xazr7iT3P8H1ljKavU2SVNX6hwYAYOo2wozrSicl2Z7kw0kOJzl/eaCqzkqyJ6M1sAAAbCGDzrhW1bVJ9mX0gatdSS5L8q1JLmytLVTV25O8uao+m2QxyfVJ7nBHAQCArWfopQJfnuQdSZ6R0QepPppRaf1f4/HXJnksyS0ZzcLenuSVA+QEAGBgQ9/H9conGT+U5FXjDQCALWwjrnEFAIC/Q3EFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC4orgAAdEFxBQCgC4orAABdUFwBAOiC4goAQBcUVwAAuqC4AgDQBcUVAIAuKK4AAHRBcQUAoAuKKwAAXVBcAQDoguIKAEAXFFcAALqguAIA0AXFFQCALiiuAAB0QXEFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC4orgAAdEFxBQCgC4orAABdUFwBAOiC4goAQBcGLa5VdXVV/UFVPVhVn6mq91TVWSvOma2qG6rqgap6qKpuqardQ2UGAGAYQ8+4vjDJDUnOTfKiJNuS/HZVPW3inOuSvDjJS8fnn57k3VPOCQDAwGaG/OGttYsm96vq+5N8JsnZSX6vquaTXJnkstbae8fnXJHk41V1bmvtg1OODADAQIaecV1pfvz42fHj2RnNwu5fPqG1dmeSA0nOO9YFqmp7Vc0tb0l2ncC8AABMyYYprlV1UpK3JHl/a+1j48OnJXm0tfa5FacfHI8dy9VJFia2e09AXAAApmzDFNeM1rr+oySXrvE612Y0c7u8nbHG6wEAsAEMusZ1WVX9pyTfnuRbWmuTM6T3Jzmlqk5dMeu6ezz2d7TWlpIsTVz7BCQGAGDaBi2uNWqV1ye5JMm3ttbuWXHKh5McTnJ+klvGrzkryZ4kd0wxKmxYrbUcOnRo6BhMyeTv2u99a5mdnTUZw5ZXrbXhfnjVLyS5LMk/T3LXxNBCa+2R8Tk3JvlnSb4/yWJGRTettW96ij9jLsnCwsJC5ubm1i88bBCPPPJILr744qFjACfYvn37smPHjqFjwAmxuLiY+fn5JJlvrS0e77yhlwrsHT/+7xXHr0jyy+Pnr03yWEYzrtuT3J7klVPIBgDABjLojOs0mHFls7NUYGtprWVpabSMf/v27d463kIsFWAz62XGFVijqvL24Razc+fOoSMADGIj3Q4LAACOS3EFAKALiisAAF1QXAEA6ILiCgBAFxRXAAC6oLgCANAFxRUAgC4orgAAdEFxBQCgC4orAABdUFwBAOiC4goAQBdmhg4wLYuLi0NHAADgGJ5qT6vW2gmOMqyqemaSe4fOAQDAkzqjtfZXxxvcCsW1kpye5MGhswCsk10Z/Q/5GfHfNmDz2JXkvvYE5XTTF1eAzaaq5pIsJJlvrVkHBWwZPpwFAEAXFFcAALqguAL0ZynJT44fAbYMa1wBAOiCGVcAALqguAIA0AXFFQCALiiuAAB0QXEFAKALiisAAF1QXAEA6ILiCgBAF/4/ss1vUAQ/Uu8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#age distribution\n",
        "plt.figure(figsize = (8,5))\n",
        "plt.hist(customer_data['age'], bins=[0,24,44,64,100])\n",
        "plt.title('Age distribution histogram')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.savefig('age_dist_histogram.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "29dZxa0GMntq",
        "outputId": "489af510-31c7-4c61-9796-fdd30fada264"
      },
      "execution_count": 475,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory data analysis for Potential Customers**\n"
      ],
      "metadata": {
        "id": "f8C2kmYkNCTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#data cleaning\n",
        "new_customers.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "Q307jcMuNflq",
        "outputId": "2658cf2a-d61b-4a51-fe54-1d088a1e8b33"
      },
      "execution_count": 476,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4bc5581a-2094-4a61-a559-47ca8778c235\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>first_name</th>\n",
              "      <th>last_name</th>\n",
              "      <th>gender</th>\n",
              "      <th>past_3_years_bike_related_purchases</th>\n",
              "      <th>DOB</th>\n",
              "      <th>job_title</th>\n",
              "      <th>job_industry_category</th>\n",
              "      <th>wealth_segment</th>\n",
              "      <th>deceased_indicator</th>\n",
              "      <th>owns_car</th>\n",
              "      <th>tenure</th>\n",
              "      <th>address</th>\n",
              "      <th>postcode</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chickie</td>\n",
              "      <td>Brister</td>\n",
              "      <td>Male</td>\n",
              "      <td>86</td>\n",
              "      <td>1957-07-12 00:00:00</td>\n",
              "      <td>General Manager</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>14</td>\n",
              "      <td>45 Shopko Center</td>\n",
              "      <td>4500</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1.71875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4bc5581a-2094-4a61-a559-47ca8778c235')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4bc5581a-2094-4a61-a559-47ca8778c235');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  first_name last_name gender  ...  property_valuation Rank    Value\n",
              "0    Chickie   Brister   Male  ...                   6    1  1.71875\n",
              "\n",
              "[1 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 476
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WB6Ag4X9NxyS",
        "outputId": "c070ab72-bb0b-4769-d6b1-b7986b185ceb"
      },
      "execution_count": 477,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 18 columns):\n",
            " #   Column                               Non-Null Count  Dtype  \n",
            "---  ------                               --------------  -----  \n",
            " 0   first_name                           1000 non-null   object \n",
            " 1   last_name                            1000 non-null   object \n",
            " 2   gender                               1000 non-null   object \n",
            " 3   past_3_years_bike_related_purchases  1000 non-null   int64  \n",
            " 4   DOB                                  1000 non-null   object \n",
            " 5   job_title                            1000 non-null   object \n",
            " 6   job_industry_category                1000 non-null   object \n",
            " 7   wealth_segment                       1000 non-null   object \n",
            " 8   deceased_indicator                   1000 non-null   object \n",
            " 9   owns_car                             1000 non-null   object \n",
            " 10  tenure                               1000 non-null   int64  \n",
            " 11  address                              1000 non-null   object \n",
            " 12  postcode                             1000 non-null   int64  \n",
            " 13  state                                1000 non-null   object \n",
            " 14  country                              1000 non-null   object \n",
            " 15  property_valuation                   1000 non-null   int64  \n",
            " 16  Rank                                 1000 non-null   int64  \n",
            " 17  Value                                1000 non-null   float64\n",
            "dtypes: float64(1), int64(5), object(12)\n",
            "memory usage: 140.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers['DOB'] = pd.to_datetime(new_customers['DOB'], errors='coerce')"
      ],
      "metadata": {
        "id": "zNbV4hD_N2kJ"
      },
      "execution_count": 478,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIyu4hwTOQsb",
        "outputId": "2b745ea0-fa73-4aed-b833-8c36cf0924d5"
      },
      "execution_count": 480,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 18 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   first_name                           1000 non-null   object        \n",
            " 1   last_name                            1000 non-null   object        \n",
            " 2   gender                               1000 non-null   object        \n",
            " 3   past_3_years_bike_related_purchases  1000 non-null   int64         \n",
            " 4   DOB                                  983 non-null    datetime64[ns]\n",
            " 5   job_title                            1000 non-null   object        \n",
            " 6   job_industry_category                1000 non-null   object        \n",
            " 7   wealth_segment                       1000 non-null   object        \n",
            " 8   deceased_indicator                   1000 non-null   object        \n",
            " 9   owns_car                             1000 non-null   object        \n",
            " 10  tenure                               1000 non-null   int64         \n",
            " 11  address                              1000 non-null   object        \n",
            " 12  postcode                             1000 non-null   int64         \n",
            " 13  state                                1000 non-null   object        \n",
            " 14  country                              1000 non-null   object        \n",
            " 15  property_valuation                   1000 non-null   int64         \n",
            " 16  Rank                                 1000 non-null   int64         \n",
            " 17  Value                                1000 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(5), object(11)\n",
            "memory usage: 140.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers['DOB'].fillna(method='ffill', inplace = True)"
      ],
      "metadata": {
        "id": "gu61uv-qOVbd"
      },
      "execution_count": 481,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_customers['age'] = new_customers['DOB'].apply(lambda x: age(x))\n",
        "new_customers.drop('DOB',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "a-s_SJSdOpa_"
      },
      "execution_count": 482,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_list = ['gender','job_industry_category','wealth_segment','deceased_indicator','owns_car','state','country']\n",
        "num_list = [elem for elem in new_customers.describe() if elem not in cat_list]"
      ],
      "metadata": {
        "id": "2QM0nAezPAo-"
      },
      "execution_count": 484,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def countplots(cat_list):\n",
        "  for elem in cat_list:\n",
        "    plt.rcParams.update({'figure.figsize':(8,5),'figure.dpi':100})\n",
        "    sns.countplot(x = new_customers[elem])\n",
        "    plt.show()\n",
        "\n",
        "countplots(cat_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8sQWQHKuO3WI",
        "outputId": "1a4e97a0-fe04-4408-a249-bd8a5672d58e"
      },
      "execution_count": 485,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAN0QbgEA6IZwCwBAN4RbAAC6say7JbBtB/3++6ZdAvyYi099/rRLAIBVZeYWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC6MdVwW1XHV9Xnquqmqrqmqj5aVQ+bN2bXqjq9qq6rqpur6qyqWj9vzH5VdXZV/XA8zqlVtePang0AANM27Znbw5KcnuTgJEcm2SnJuVV1n4kxb03ytCTPHsfvk+Qjc51VtUOSs5PsnOSQJC9IckySE1e/fAAAZslUZzdba0dPPq+qY5Jck+SgJJ+uqt2TvCjJc1tr549jjk3y1ao6uLX22SRHJXlEkie11jYluaSqXpfklKp6Q2vt1rU7IwAApmnaM7fz7T4+Xj8+HpRhNve8uQGttcuTfCfJhrFpQ5JLx2A755wk65I8cqEXqapdqmrd3JZkt5U7BQAApmVmwm1V3SvJ25Jc2Fr78ti8V5JbW2s3zBu+aeybG7Npgf5MjJnv+CQ3TmxXLqN0AABmxMyE2wxrbx+V5Dlr8FonZ5glntv2XYPXBABglc3EHQWq6h1JnprkCa21yVnUq5PsXFV7zJu9XT/2zY157LxDrp/o+zGtta1Jtk68/jKqBwBgVkz7VmA1BttnJPml1tq/zBtycZIfJTliYp+HJdkvycaxaWOSA6pqz4n9jkyyOcllq1U7AACzZ9ozt6cneW6SX01yU1XNrZG9sbW2pbV2Y1WdkeS0qro+Q2B9e5KN450SkuTcDCH2/VX16gzrbE9Kcvo4QwsAwD3EtMPtS8bHC+a1H5vkvePPr0xyR5KzkuyS4U4IL50b2Fq7vaqemuRdGWZxf5DkzCQnrFbRAADMpmnf5/ZuF7u21m5Jcty4bWvMFUmesoKlAQCwHZqluyUAAMCyCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC6IdwCANAN4RYAgG4ItwAAdEO4BQCgG8ItAADdEG4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC6IdwCANAN4RYAgG4ItwAAdEO4BQCgG8ItAADdEG4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN2YaritqidU1ceq6qqqalX19Hn9VVUnVtX3q2pLVZ1XVfvPG3P/qvpAVW2uqhuq6oyquu/angkAALNg2jO390nyxSTHbaP/1UlenuTFSR6X5AdJzqmqXSfGfCDJI5McmeSpSZ6Q5M9Xq2AAAGbXjtN88dbax5N8PEmq6k59NTS8IslJrbW/Hduen2RTkqcn+cuqeniSo5M8prX2+XHMy5L8XVX9XmvtqrU6FwAApm/aM7d35cFJ9kpy3lxDa+3GJBcl2TA2bUhyw1ywHZ2X5I4MM70Lqqpdqmrd3JZkt5UuHgCAtTfL4Xav8XHTvPZNE317JblmsrO1dluS6yfGLOT4JDdObFcut1gAAKZvlsPtajo5ye4T277TLQcAgJUw1TW3d+Pq8XF9ku9PtK9PcsnEmD0nd6qqHZPcf2L/H9Na25pk68Q+K1AuAADTNsszt/+SIaAeMdcwro99XJKNY9PGJHtU1UET+/1ShvO6aI3qBABgRkx15na8H+1DJ5oeXFUHJrm+tfadqnpbkj+qqm9kCLtvSnJVko8mSWvtq1X1iSTvrqoXJ9kpyTuS/KU7JQAA3PNMe1nCLyb51MTz08bHM5Mck+QtGe6F++dJ9kjymSRHt9ZumdjneRkC7Scz3CXhrAz3xgUA4B5m2ve5vSDJNhe8ttZakhPGbVtjrk/y3BUvDgCA7c4sr7kFAIBFEW4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC6IdwCANAN4RYAgG4ItwAAdEO4BQCgG8ItAADdEG4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC6IdwCANAN4RYAgG4ItwAAdEO4BQCgG8ItAADdEG4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcAAHRDuAUAoBvCLQAA3RBuAQDohnALAEA3hFsAALrRTbitquOq6ttVdUtVXVRVj512TQAArK0uwm1V/XqS05K8MckvJPliknOqas+pFgYAwJrqItwm+d0k726t/UVr7bIkL07ywyQvnG5ZAACspR2nXcByVdXOSQ5KcvJcW2vtjqo6L8mGbeyzS5JdJpp2S5LNmzevWF23b92yYseClbKS/4+vlptuuX3aJcCdbA/vmyS5bctt0y4B7mSl3zs/6fGqtbaiL7zWqmqfJN9LckhrbeNE+1uSHNZae9wC+7whyevXrEgAAFbKvq21722rc7ufuV2ikzOs0Z10/yTXT6EW7tpuSa5Msm+Sm6ZcC2xPvHdg8bxvZt9uSa66qwE9hNt/TXJ7kvXz2tcnuXqhHVprW5Nsnde8ffzd6R6mquZ+vKm15r8R/IS8d2DxvG+2C3f732W7v6CstXZrkouTHDHXVlX3Gp9v3NZ+AAD0p4eZ22RYYnBmVX0+yT8leUWS+yT5i6lWBQDAmuoi3LbWPlRVD0hyYpK9klyS5OjW2qbpVsYK2Jrh/sXzl5EAd817BxbP+6YD2/3dEgAAYM52v+YWAADmCLcAAHRDuAUAoBvCLQAA3RBuAWZYVb23qlpVvXZe+9Orqk08/+2q+mJV3VxVN1TV/6uq48e+o8dj7DXvGN+vqm/Pa/uZcewRgU5U1ceq6hPb6Hv8+P/8z4+PB87rf2ZVXVBVN47vry9V1QlVdf+1qZ7FEm5Zc1X1wKp6T1VdVVW3VtUVVfWnVfXTE2MuqKq33cUx2sT2g6r6xhgCDlqbs4A1dUuS11TV/RbqrKoXJnlbkj9LcmCSQ5O8Jcl9xyGfSXJbksMn9nl4knsnuV9V/czE4Z6Y4TZIF67kCcCUnZHkyKrad4G+Y5N8Pgt881VVvTnJh5J8LskvJ3lUklcleXSS31q1alkW4ZY1VVU/m+FDZP8kv5HkoUlenPEb5Rb5L+Fjk+yd5JFJjsvwi/yiqnr+ihYN03dehq8TP34b/f8lyV+11s5orX2ztfaV1toHW2t/mCSttZsz/HI+fGKfwzOE3gsXaP9sa+2WlTwBmLL/k+TaJMdMNlbVfZM8O0P4zby+xyb5gySvaq39fmvtH1tr326t/X1r7ZlJzlz9slkK4Za1dnqSW5Mc1Vr7h9bad1prH0/ypCT/IcmbF3GsG1prV48fNue21p6V5ANJ3rGtGS7YTt2e4Zfsy7Yx83R1koOr6kF3cYxPZZiVnfPEJBck+Yd57YePY6EbrbXbkrwvyTFVVRNdz06yQ5IPLrDb85LcnOSd2zjmDStdJytDuGXNjLOyT07yztbalsm+1trVGYLpr8/74FmstybZLcmRyzgGzJzW2t9k+PbFNy7Q/cYkNyT5dlV9bVyi82tVNfkZ/6kkP1dVe4/PD8sQbD89/jz3l5X9ItzSp/ckeUjG/99HxyY5q7V24wLj90/yrdbaj9aiOFaOcMta2j9JJfnqNvq/muR+SR6wjNe4fHz8mWUcA2bVa5K8YFwv++9aa99vrW1IckCSP83w1epnJvnERMD9xwx/NTm8qh6RYb3tFzIsE3pAVT04w6ztliSfXYNzgTXVWrs8w/vghUlSVQ9N8vgssCRhtJyJFqZIuGUa7u4D49YVOLbvlaY7rbVPJzknycnb6P9ya+2drbXfzPDXiyMzzlK11n6Y5J8yLEF4YpLPtNZuH2el/nGi/cLW2nLegzDLzkjyzKraLcOs7T9n+AvGQr6e5Geraqe1Ko6VIdyylr6ZIXQ+fBv9D09y7TLXMc0d+1+WcQyYZa9N8rQkG+5m3GXj430m2j6VYXb28Azrbed8emw7LJYk0Le/SnJHkucmeX6S97TWtjUZ8r8yXKj80oU6q2qPVamQZRNuWTOtteuS/H2Sl1bVvSf7xvtvPi/Je5f5Mq/IcDuX85Z5HJhJrbVLM6xPf/lcW1W9q6peV1WHVtWDqurgDBfPXJtk48Tun8qwPOjJufNs1T8keXqSB0a4pWPjnUM+lOGvH3vnLn7ntNYuynBLvT+pqrdU1Ybx/XVEVX04yQvWomYWT7hlrf23JLskOaeqnjDe8/boDKH360lOnBj7gKo6cN62fqJ/j6raa/ywObKq/jrDv8Zf4ipWOndC7vz5fV6Sg5N8OMP76KwM98Y9YvxH5ZyNGe5hW0kunmi/KMlOGa4M/9zqlQ0z4YwM13ec01q76q4GttZek+H3yuMyLAn6SpLTknwpbgU2s2rbs/GwOsYbxr8hydFJ9szwi/YjSX5rXBeYqrogd76idc7rWmsnTX4zU4Zf4t/LcM/OP2utfWG1agcAZptwy9RV1RuT/G6SI1trrtIGAJZMuGUmVNWxSXbPMPN6x7TrAQC2T8ItAADdcEEZAADdEG4BAOiGcAsAQDeEWwAAuiHcAgDQDeEWAIBuCLcA25mqem9VfXQJ+72hqi5ZjZoAZoVwCwBAN4RbgBlVVc+qqkuraktVXVdV51XVqUlekORXq6qN2+Hj+FOq6utV9cOq+lZVvamqdhr7jkny+iSPntjvmLFvj6r6n1V1bVVtrqrzq+rRUzlpgGXacdoFAPDjqmrvJB9M8uokf5NktySPT/K+JPslWZfk2HH49ePjTUmOSXJVkgOSvHtse0uSDyV5VJKjkzxpHH/j+PjhJFuS/PLY9jtJPllVP9damzs2wHZBuAWYTXtn+Iz+SGvtirHt0iSpqi1JdmmtXT25Q2vtpImn366qP07ynCRvaa1tqaqbk9w2uV9V/eckj02yZ2tt69j8e1X19CTPSvLnq3BuAKtGuAWYTV9M8skkl1bVOUnOTfLXrbV/29YOVfXrSV6e5CFJ7pvhM37z3bzOo8ex11XVZPu9x+MAbFeEW4AZ1Fq7vaqOTHJIkqOSvCzJm6vqcQuNr6oNST6QYV3tORmWFzwnyavu5qXum+T7SQ5foO+GJRUPMEXCLcCMaq21JBcmubCqTkxyRZJnJLk1yQ7zhh+S5IrW2pvnGqrqQfPGLLTfF5LslWG5wrdXrnqA6RBuAWbQOEN7RIblCNckeVySByT5apJdkzy5qh6W5LoMs7TfSLJfVT0nyeeS/EqGIDzp20keXFUHJrkyw8Vm5yXZmOSjVfXqJF9Pss+4/9+01j6/iqcJsOLcCgxgNm1O8oQkf5chcJ6U5FWttY9nuAvC15J8Psm1SQ5trf3vJG9N8o4kl2SYyX3TvGOeleQTST417vcb4+zwU5J8OslfjK/1l0kelGTTKp4fwKqo4XMNAAC2f2ZuAQDohnALAEA3hFsAALoh3AIA0A3hFgCAbgi3AAB0Q7gFAKAbwi0AAN0QbgEA6IZwCwBAN4RbAAC68f8BGOlbnZFevZwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def boxplots(num_list):\n",
        "  for elem in num_list:\n",
        "    plt.rcParams.update({'figure.figsize':(8,5),'figure.dpi':100})\n",
        "    sns.boxplot(y = new_customers[elem])\n",
        "    plt.show()\n",
        "\n",
        "boxplots(num_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tzWl5J8kP3Ja",
        "outputId": "3c5abb48-3b53-41ae-ae8f-2cb8ba66dbfd"
      },
      "execution_count": 486,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wcH/HbUcAhwIHArsCDwPnR8RQUzkkSZK0Ziiduf0YcGBmvpeqeI66hKrsNuXvgNszc//M/Elm3pyZ383MX0E1awscBnwqM8/JzGuBdwObAW9tMIckSZLWAKXldnvgB+OMrwQ2KI/zW94MLIuI/xcRd0fEVRHRuQXZNsAmwIWjA5m5kmoHh4UTfWhEzI6I+aMvYF6DmSVJkjQgpeX2TuAF44y/Bvh1eZzf8t+Ag4CbgL2AfwQ+HxH71tc3qX/eNeZ9d3VcG88SqiI++lreVGBJkiQNTmm5/SfgpIjYFUhgs4h4F/BZqgLalHWAKzPzyMy8KjNPrX/3gT1+7rFU64NHX1v0+HmSJEmaBkq3AvsMVfG8CJhLtURhBPhsZp7cUDaAO4Abxoz9jKcfFHFn/XPj+l46zq+e6EMzc4QqLwDV0l1JkiSt6Uof4pCZ+WmqhzfsALwS2CgzP95kOKovqG0/Zmw74Nb6+GaqgrvH6MV6De2uwKUNZ5EkSdI0V7oV2GkRMS8zH8vMG+qdDB6KiPUi4rQG850AvDIijoyIF0TEPsD7gFOgKtnAicDHIuLNEfGHwFeBFcC3GswhSZKkNUDpmtt9gTnjjM+h2oqrEZn5n8Bi4J3A9cDHgcMy84yO244DTgZOBf4TWB/YOzOHm8ohSZKkNUNXa27rv/KP+jUvIjoL5Azgj4G7m4sHmfnvwL8/y/UEPlG/JEmStBbr9gtlD1DtjpDAL8a5nsAnew0lSZIklei23O5GNWt7MdWOBfd1XHsMuDUzVzSUTZIkSepKV+U2M/8DICK2oXos7uopSSVJkiQVKNrnNjNvBYiIucBWwKwx16/tPZokSZLUnaJyGxEbAf8MLJrglhnFiSRJkqRCpVuBnQhsQPWwhEeBvam2B7sJeHMz0SRJkqTulD5+d3fgLZm5LCJWU32R7IKIWAUsAb7TWEJJkiRpkkpnbtfj6f1s7wc2qo+vA3bqNZQkSZJUorTc3ghsXx9fA7w/IjYHDgTuaCKYJEmS1K3SZQknAZvWx0cB5wHvotrrdr/eY0mSJEndK90K7PSO4ysiYmvghcBtmXlPU+EkSZKkbpTO3D5DZj4CXNnEZ0mSJEmlJl1uI+L4yd6bmYeXxZEkSZLKdTNz+7JJ3pclQSRJkqReTbrcZuZuUxlEkiRJ6lXpVmAARMQLImKviJhTn0czsSRJkqTuFZXbiHheRFwE/AI4l6e3BftSRHyuqXCSJElSN0pnbk8AHge2Ah7pGP86sHevoSRJkqQSpVuB7QnslZnLx6xEuAnYuudUkiRJUoHSmdv1eOaM7agNgZHyOJIkSVK50nL7Q+DdHecZEesARwDf6zmVJEmSVKB0WcIRwEUR8XJgFnAc8GKqmdtXN5RNkiRJ6kpRuc3M6yNiO+AQ4EFgfeAs4JTMvKPBfJoimR3P2njy8cEFkSRJk9fx3+xn/LdcT+m63EbETOA84MDM/HTzkdQPIyNPL42ed83XBphEkiSVGBkZYe7cuYOOMe10veY2Mx8HXjIFWSRJkqSelK65PR04APhIg1nUR7Nnz37q+MGXvgNmzBxgGkmSNClPPv7U37h2/rdcTystt+sC74mINwBXAA93XszMw3sNpqn1jP2JZ8y03EqStIYZ86wB1UrL7Q7AlfXxdmOuubpZkiRJA1G6W8Juk7kvIrYAVmTm6pLfI0mSJHWj9CEOk3UD8HtT/DskSZIkYOrLrYtBJEmS1DdTXW4lSZKkvrHcSpIkqTUst5IkSWqNqS63bgsmSZKkvvELZZIkSWqNnsptRLwgIvaKiDn1+dgy+wfArb38DkmSJGmyisptRDwvIi4EfgGcC2xaX/pSRHxu9L7MvD0zn+w9piRJkvTcSmduTwCeALYCHukY/zqwd6+hJEmSpBJFj98F9gT2yszlY1Yi3ARs3XMqSZIkqUDpzO16PHPGdtSGwEh5HEmSJKlcabn9IfDujvOMiHWAI4Dv9ZxKkiRJKlC6LOEI4KKIeDkwCzgOeDHVzO2rG8omSZIkdaVo5jYzrwe2A34EnEO1TOEs4GWZ+avm4kmSJEmTVzRzGxG7Zeb3gE+Pc+3gzDyl52SSJElSl0rX3J4VETuPHYyIDwLH9hZJkiRJKlNabv8WWBoRLxwdiIgPA0cDb2oimCRJktStomUJmfnFiNgQuDAiXgP8BXAk8MeZeUmTASVJkqTJKt0tgcw8LiKeBywDZlA91OGyxpJJkiRJXZp0uY2IQ8cZ/g3Vwxx+AOwSEbsAZObnm4knSZIkTV43M7cfmmD8Saq9bUf3t03AcitJkqS+m3S5zcxtpjKIJEmS1KvS3RIkSZKkaaebNbfHAx/PzIfr4wll5uE9J5MkSZK61M2a25cBMzuOJ5LlcSRJkqRy3ay53W28Y0mSJGm66HnNbURsGRFbNhFGkiRJ6kVRuY2IdSPimIhYCdwC3BIRKyPiUxEx8zneLkmSJE2J0pnbk4H3AUdQrb99WX18AFO4x21EfCQiMiJO7BgbiohTIuLeiHgoIr4ZERtPVQZJkiRNX6WP390HeEdmLu0YuzYibgfOBA7qOdkYEfEK4P3AtWMunQC8CfhzYCXwBeAsnn6ohCRJktYSpTO3I1TLEca6GXisOM0EImJ94AzgvcD9HeMLqGaLD8/MizPzCmB/4FUR8cqmc0iSJGl6Ky23XwA+HhGzRwfq44/W15p2CvCdzLxwzPjOVNuTPTWemT8HbgMWTvRhETE7IuaPvoB5U5BZkiRJfdbNQxzOGjP0BmB5RFxTn78UmAVc1FC20d/7DmAn4BXjXN4EeCwzHxgzfld9bSJLgE82k1CSJEnTRTdrbleOOf/mmPPbe8zyW+otxk4C3piZww1+9LFA51PW5gHLG/x8SZIkDUA3D3HYv9sPj4hXA8syc6Tb99Z2Bp4PXBkRo2MzgNdFxCHAXsCsiNhgzOztxsCdE31oneepTB2fLUmSpDVYzw9xeA5Lgc17eP9FwB8CO3a8llF9uWz0+HFgj9E3RMT2wFbApT38XkmSJK2BSrcCm6yepkQz80Hg+md8YMTDwL2ZeX19/iXg+Ii4D1hFtQfvpZl5WS+/W5IkSWueqS63/fAhYDXVGuDZwPnA3ww0kSRJkgZijSu3mfn6MefDwMH1S5IkSWuxqV5zK0mSJPXNVJfbnOLPlyRJkp4y1eXWPbYkSZLUN0VrbiNiDhCZ+Uh9vjWwGLghM787el9m+lhbSZIk9U3pzO05wLsBImID4HLgw8A5EXFQQ9kkSZKkrpSW252AH9bHfwbcBWxNVXgPbSCXJEmS1LXScjsXeLA+3hM4KzNXA5dRlVxJkiSp70rL7S+Bt0bElsBewOg62+dTPSVMkiRJ6rvScns08FngFuDyzLy0Ht8TuKqBXJIkSVLXinZLyMxvRMSPgE2BazouXQSc3UQwSZIkqVtdl9uImAk8CuyYmc+Ypc3MnzQVTJIkSepW18sSMvNx4DZgRvNxJEmSpHKla24/DfxDRGzYZBhJkiSpF0VrboFDgBcAKyLiVuDhzouZuVOvwSRJkqRulZbbbzWaQpIkSWpA6W4JRzUdRJIkSepV6ZpbSZIkadopmrmNiBnAh4C3A1sBszqvZ6ZfNJMkSVLflc7cfhI4HPg6sAA4HjgLWA38fSPJJEmSpC6Vltt3Ae/NzM8BTwBnZuZfUz2W95VNhZMkSZK6UVpuNwGuq48fopq9Bfh34E29hpIkSZJKlJbb5cCm9fGvgD3r41cAI72GkiRJkkqUltuzgT3q45OBYyLiJuCrwGlNBJMkSZK6VbrP7Uc6jr8eEbcBC4GbMvPbTYWTJEmSulH6hLJnyMxLgUub+CxJkiSpVPFDHCLiryLikohYERFb12OHRcRbmosnSZIkTV5RuY2Ig6j2tj0X2ACYUV96ADismWiSJElSd0pnbj9Atc/tp4EnO8aXAX/YcypJkiSpQGm53Qa4apzxEWC98jiSJElSudJyezOw4zjjewM/K48jSZIklSvdLeF44JSIGAIC2CUi3gksAf66qXCSJElSN0r3uf1iRDwKfAqYC/wLsAL4YGZ+rcF8kiRJ0qQV73ObmWcAZ0TEXGD9zLy7uViSJElS90q3AjtqdG/bzHzEYitJkqTpoPQLZW8BfhURF0XEPhExu8lQkiRJUomicpuZOwKvAH4KnATcGRH/GBGvaDKcJEmS1I3ix+9m5lWZeSiwGXAAsAVwSURcGxEfjIgFTYWUJEmSJqP4C2UdApgJzKqP7wcOAY6JiPdm5tcb+B2aQrH6CXLQISQ1JxNWP1Edr7MuRAw2j6TGxOifbU2ouNxGxM7A/sA7qZ5M9lXg4Mz8ZX39A8DnAcvtNLf+1WcOOoIkSVIjSndLuA64jOoxvAcAW2bmR0aLbe1MYKPeI0qSJEmTUzpz+6/AaZn5m4luyMx76GFNr6bW0NAQS5cuHXQMSVNgeHiYxYsXA3D22WczNDQ04ESSpoJ/tsdX+oSyYyZzX0SsAnbMzF+X/B5NnYhgzpw5g44haYoNDQ35Z13SWmWqZ1b9FoMkSZL6xmUDkiRJag3LrSRJklrDcitJkqTWmOpy67MBJEmS1Dd+oUySJEmtMdXldhEw4V64kiRJUpO6LrcR8caIOCoidq/PXxcRSyPi4ojYv/PezPxRZo40FVaSJEl6Nl2V24j4S+Bc4E+AcyJiP+AcYDlwM/C/I+LPmg4pSZIkTUa3Tyj7MPDhzPx8ROwBfBv4aGaeABARNwCHAd9oNqYkSZL03LpdlrAtVaElMy+iKscXdVz/DvDCZqJJkiRJ3em23D4OzOo4HwEeGnPuQ8wlSZI0EN2W21/yzJnZzanW2o76far1t5IkSVLfdbvm9h+A+0dPMnPVmOsvB/6111CSJElSia7KbWae/RzXP9N5HhGvBpa5HZgkSZL6Yaof4rCUaumCJEmSNOV8/K4kSZJaY6rLbc8iYklE/GdEPBgRd0fEtyJi+zH3DEXEKRFxb0Q8FBHfjIiNB5VZkiRJgzHtyy3wR8ApwCuBNwIzge9GxHod95wA/Cnw5/X9mwFn9TmnJEmSBqzb3RL6LjP37jyvH/l7N7Az8IOIWAAcAOyTmRfX9+wP/CwiXpmZl/U5siRJkgZkqmducwo+c0H98776585Us7kXPvVLM38O3AYsHO8DImJ2RMwffQHzpiCnJEmS+myN+kJZRKwDnAhckpnX18ObAI9l5gNjbr+rvjaeJcDKjpcPnpAkSWqBonIbERdHxAbjjM+PiItHzzNzXmb+upeAY5wC7AC8o8fPOZZqBnj0tUWPnydJkqRpoHTN7euBWeOMDwGvLU7zLCLiC8CfAK/LzM6Z1juBWRGxwZjZ243ra7+lfqjEUw+WiHDHMkmSpDboqtxGxEs6Tv8gIjr/2n8GsDfwmyaCdfzOAE4GFgOvz8ybx9xyBfA4sAfwzfo92wNbAZc2mUWSJEnTW7czt1dTfUksgYvHuf4o8IFeQ41xCrAP8BbgwY5CvTIzH83MlRHxJeD4iLgPWEVVhi91pwRJkqS1S7fldhuqL4n9GtgF+P8d1x4D7s7MJxvKNuqg+uf3x4zvD3y5Pv4QsJpq5nY2cD7wNw3nkCRJ0jTXVbnNzFvrw749/CEzn3NBbGYOAwfXL0mSJK2lSndL2Dci3tRxflxEPBARP46IrZuLJ0mSJE1e6QzskVTra4mIhcAhwBHAPVSPwpUkSZL6rnQrsC2BX9bHbwW+kZmnRsQl/PbaWEmSJKkvSmduHwKeVx/vCVxQHw8Dc3oNJUmSJJUonbm9APhiRFwFbAecW4+/GLilgVySJElS10pnbg+mekDCRsDbMvPeenxn4MwmgkmSJEndKpq5rR9ze8g445/sOZEkSZJUqHRZAgARMZfqMbezOscz89pePleSJEkqUVRuI2IjqqeD7T3BLTNKA0mSJEmlStfcnggsAHal2u92b2Bf4Cbgzc1EkyRJkrpTuixhd+AtmbksIlYDt2bmBRGxClgCfKexhJIkSdIklc7crgfcXR/fT7VrAsB1wE69hpIkSZJKlJbbG4Ht6+NrgPdHxObAgcAdTQSTJEmSulW6LOEkYNP6+CjgPOAvgceo1t5KkiRJfVe6z+3pHadXAlsDLwRuy8x7mue3sJwAAAQqSURBVAgmSZIkdat0WQIRcUBEXA8MU627/Srw1qaCSZIkSd0q3ef2aOBw4GSqx/ACLAROiIitMvMTDeWTJEmSJq10ze1BwHsz88yOsX+LiGupCq/lVpIkSX1XuixhJrBsnPEr6PGRvpIkSVKp0nL7f6lmb8d6H3BGeRxJkiSpXC+zrAdExJ7AZfX5rsBWwFcj4vjRmzLz8B5+hyRJkjRppeV2B6otwAB+v/55T/3aoeO+LPx8SZIkqWul+9zu1nQQSZIkqVfF+9xKkiRJ043lVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrWG4lSZLUGpZbSZIktYblVpIkSa1huZUkSVJrtKbcRsTBEXFLRAxHxOURscugM0mSJKm/WlFuI+IvgOOBo4CdgGuA8yPi+QMNJkmSpL5ad9ABGnI48E+Z+c8AEXEg8CbgPcBnBhlMmg4yk+Hh4UHHUJ90/rP2n/vaZWhoiIgYdAxpoNb4chsRs4CdgWNHxzJzdURcCCyc4D2zgdkdQ/OmNKQ0YMPDwyxatGjQMTQAixcvHnQE9dHSpUuZM2fOoGNIA9WGZQm/C8wA7hozfhewyQTvWQKs7Hgtn7J0kiRJ6ps1fua20LFUa3RHzcOCqxYbGhpi6dKlg46hPslMRkZGAJg9e7Z/Tb0WGRoaGnQEaeDaUG7vAZ4ENh4zvjFw53hvyMwRYGT03H/xq+0iwr+qXMvMnTt30BEkaSDW+GUJmfkYcAWwx+hYRKxTn186qFySJEnqvzbM3EK1xOArEbEM+AlwGLAe8M8DTSVJkqS+akW5zcyvR8RGwNFUXyK7Gtg7M8d+yUySJEkt1opyC5CZXwC+MOgckiRJGpw1fs2tJEmSNMpyK0mSpNaw3EqSJKk1LLeSJElqDcutJEmSWsNyK0mSpNaw3EqSJKk1LLeSJElqDcutJEmSWqM1TyhrwqpVqwYdQZIkSeOYbE+LzJziKNNfRGwOLB90DkmSJD2nLTLzNxNdtNwCERHAZsCDg84iSQ2ZR/U/2rfAf7dJao95wIp8lgJruZWkFoqI+cBKYEFmuuZK0lrDL5RJkiSpNSy3kiRJag3LrSS10whwVP1TktYarrmVJElSazhzK0mSpNaw3EqSJKk1LLeSJElqDcutJEmSWsNyK0mSpNaw3EqSJKk1LLeSJElqDcutJEmSWuO/AOu5r2R7GUT9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nziRJ7rnnnsxms4knAg6C3+0LE65XoKrKiRMnph4DOGCz2czvOnCkWCoAAEALwhUAgBaEKwAALQhXAABaEK4AALQgXAEAaEG4AgDQgnAFAKAF4QoAQAvCFQCAFoQrAAAtCFcAAFoQrgAAtCBcAQBoQbgCANCCcAUAoAXhCgBAC8IVAIAWhCsAAC0IVwAAWhCuAAC0IFwBAGhBuAIA0IJwBQCghZUO16r67qoaO7bfm3ouAAAO3/GpB7gIv5PkaxauPz7VIAAATKdDuD4+xnh46iEAAJjWSi8V2PIFVfVgVX2kqv5TVT1/rztX1VpVndrekpw8pDkBADhAqx6uH0zy+iS3J3lTkluS/HJV7RWjdyQ5u7A9cMAzAgBwCFY6XMcY62OM/zzG+PAY494kfz3J9Un+7h4PuzPJdQvbzQc/KQAAB63DGtenjDE+VVW/n+TWPe6zmWRz+3pVHcZoAAAcsJU+4rpTVV2b5POTPDT1LAAAHK6VDteququqXlVVf6mqviLJPUmeSPKuiUcDAOCQrfpSgZszj9TnJPlEkl9J8vIxxicmnQoAgEO30uE6xnjt1DMAALAaVnqpAAAAbBOuAAC0IFwBAGhBuAIA0IJwBQCgBeEKAEALwhUAgBaEKwAALQhXAABaEK4AALQgXAEAaEG4AgDQgnAFAKAF4QoAQAvCFQCAFoQrAAAtCFcAAFoQrgAAtCBcAQBoQbgCANCCcAUAoAXhCgBAC8IVAIAWhCsAAC0IVwAAWhCuAAC0IFwBAGhBuAIA0IJwBQCgBeEKAEALwhUAgBaEKwAALQhXAABaEK4AALQgXAEAaEG4AgDQgnAFAKAF4QoAQAvCFQCAFoQrAAAtCFcAAFoQrgAAtCBcAQBoQbgCANCCcAUAoAXhCgBAC8IVAIAWhCsAAC0IVwAAWhCuAAC0IFwBAGhBuAIA0IJwBQCgBeEKAEALwhUAgBaEKwAALQhXAABaEK4AALQgXAEAaEG4AgDQgnAFAKAF4QoAQAvCFQCAFoQrAAAtCFcAAFoQrgAAtCBcAQBooUW4VtWbq+p/V9VGVX2wqr586pkAADhcKx+uVfX3ktyd5HuSfFmSDyW5t6o+d9LBAAA4VMenHuAifFuSHx1jvCNJquqNSf5Gkm9O8n1TDgarYIyRjY2NqcfgkCz+rP3cj5bZbJaqmnoMmNRKh2tVPTvJS5Lcub1vjPFkVb0vySt2ecxakrWFXScPdEiY2MbGRk6fPj31GEzgzJkzU4/AIVpfX8+JEyemHgMmtepLBT4nybEkj+zY/0iS5+7ymDuSnF3YHjiw6QAAODQrfcR1SXdmviZ228mIV65gs9ks6+vrU4/BIRljZHNzM0mytrbmpeMjZDabTT0CTG7Vw/WPkzyR5IYd+29I8vCFHjDG2EyyuX3dP+pc6arKy4dHzNVXXz31CACTWOmlAmOMx5L8VpLbtvdV1VVb1++bai4AAA7fqh9xTeYv+7+zqn4zya8n+ZYk1yR5x6RTAQBwqFY+XMcYP1lVfyHJWzN/Q9b9SW4fY+x8wxYAAFewlQ/XJBljvC3J26aeAwCA6az0GlcAANgmXAEAaEG4AgDQgnAFAKAF4QoAQAvCFQCAFoQrAAAtCFcAAFoQrgAAtNDik7P2w7lz56YeAQCAC7jYTqsxxgGPMq2q+rwkD0w9BwAAz+jmMcYf7XbjUQjXSnJTkkenngVgn5zM/D/kN8e/bcCV42SSB8cecXrFhyvAlaaqTiU5m+S6MYZ1UMCR4c1ZAAC0IFwBAGhBuAL0s5nke7YuAY4Ma1wBAGjBEVcAAFoQrgAAtCBcAQBoQbgCANCCcAUAoAXhCgBAC8IVAIAWhCsAAC38KQsn3nLS04fpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kj8voY21/OclHW2t/vdrDAQDAkSw5ZqtqOsnPJ7knyTVJPtBa+/O1GgwAAI5mOXdmv5fkJUmua60dXKN5AABgyZbzaK4XruUgAACwXA/paQYAADAkMQsAQLfELAAA3RKzAAB0S8wCANAtMQsAQLfELAAA3RKzAAB0S8wCANAtMQsAQLfELAAA3RKzAAB0S8wCANAtMQsAQLfELAAA3RKzAAB0S8wCANAtMQsAQLfELAAA3doy9ACsvdbaD3YOfm+4QQCApZv3b/aD/i3nQcTsBjA7O/v9r7ff+l8HnAQAWInZ2dkcf/zxQ4+xLllmAABAt9yZ3QC2bt36/a/v/ZmXJZsfNuA0AMCSHPze93+iOv/fch5MzG4AVfWDnc0PE7MA0JkH/VvOg1hmAABAt8QsAADdErMAAHRLzAIA0C0xCwBAt8QsAADdErMAAHRrXcRsVV1QVbdV1UxV3VxVTznK+RdW1Z9X1X1VdXtVvbOqpiY1LwAA68PgMVtVL01yeZLLkvxskluT3FBVP7rI+a9I8vbx+U9K8uokL03yGxMZGACAdWPwmE1yUZL3ttauaq19Ocn5Sb6b5LxFzn9aks+21j7UWruttfaJJB9OcsS7uQAAHHsGjdmqOi7J6Uk+NXestXZovP/URS774ySnzy1FqKpTkzw/yccXeY2tVbVjbkuyfRX/EwAAGNCWgV//EUk2J9m34Pi+JE883AWttQ9V1SOS/M8afVDxliTvaa0ttszg4iRvXqV5AQBYR9bDMoNlqaqzkrwpyesyWmP74iQvqKp/s8gle5PsnLftmsCYAABMwNB3Zu9JcjDJSQuOn5Tk24tc8+tJfru19r7x/p9W1QlJ/lNVvW28TOH7WmuzSWbn9kc3cwEAOBYMeme2tXZ/ki8kedbcsaraNN6/aZHLjk9yaMGxg3OXr/aMAACsX0PfmU1Gj+W6uqo+n+RzSS5MckKSq5Kkqq5Jcmdr7eLx+dcmuaiqvpjk5iSPy+hu7bWttYMLf3MAAI5dg8dsa+0jVfXIJG9JcnKSW5I8r7U296aw3Xnwndi3JmnjXx+d5O6MAvfXJjY0AADrwuAxmySttSuTXLnI985asP9ARh+YcNnaTwYAwHrW3dMMAABgjpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDolpgFAKBbYhYAgG6JWQAAuiVmAQDo1pahB2Cy6tADaUMPAaye1pJDD4y+3rQlqRp2HmDV1NzfbY5IzG4wJ97y4aFHAABYNZYZAADQLXdmN4CpqalMT08PPQawBmZmZrJnz54kycc+9rFMTU0NPBGwFvzdXpyY3QCqKtu2bRt6DGCNTU1N+bsObDiWGQAA0C0xCwBAt8QsAADdErMAAHRLzAIA0K11EbNVdUFV3VZVM1V1c1U95SjnP7yq3lVV36qq2ar6alU9f1LzAgCwPgz+aK6qemmSy5Ocn+TmJBcmuaGqfrK1dtdhzj8uySeT3JXkJUnuTPJjSf5yYkMDALAuDB6zSS5K8t7W2lVJUlXnJ3lBkvOSvP0w55+X5G8keVpr7XvjY7dNYE4AANaZQZcZjO+ynp7kU3PHWmuHxvtPXeSyFya5Kcm7qmpfVX2pqt5UVZsXeY2tVbVjbkuyfXX/KwAAGMrQa2YfkWRzkn0Lju9LcvIi15ya0fKCzUmen+TXk/xqkksWOf/iJPvnbXc8tJEBAFgvho7ZldiU0XrZ17bWvtBa+0iSt2W05vZw9ibZOW/bNZEpAQBYc0Ovmb0nycEkJy04flKSby9yzbeSfK+1dnDesa8kObmqjmut3T//5NbabJLZuf2qeshDAwCwPgx6Z3Ycnl9I8qy5Y1W1abx/0yKXfTbJ48bnzXlCkm8tDFkAAI5t62GZweVJXlNVv1RVT0ry7iQnJJl7usE1VbV33vnvzuhpBldU1ROq6gVJ3pTkXROeGwCAgQ29zCCttY9U1SOTvCWjN33dkuR5rbW5N4XtTnJo3vm3V9Vzk7wzyZ9k9JzZK5L8u4kODgDA4AaP2SRprV2Z5MpFvnfWYY7dlOTvrvFYAACsc+thmQEAAKyImAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbolZAAC6JWYBAOiWmAUAoFtiFgCAbq2LmK2qC6rqtqqaqaqbq+opS7zuZVXVqur31npGAADWn8FjtqpemuTyJJcl+dkktya5oap+9CjX/XiS30rymTUeEQCAdWrwmE1yUZL3ttauaq19Ocn5Sb6b5LzFLqiqzUk+mOTNSf73RKYEAGDdGTRmq+q4JKcn+dTcsdbaofH+U49w6b9Ncldr7f1LeI2tVbVjbkuy/SGODQDAOjH0ndlHJNmcZN+C4/uSnHy4C6rqGUleneQ1S3yNi5Psn7fdsaJJAQBYd4aO2WWpqu1JfjvJa1pr9yzxsr1Jds7bdq3ReAAATNiWgV//niQHk5y04PhJSb59mPN/IsmPJ7m2quaObUqSqnogyU+21v7X/Ataa7NJZuf2510HAEDnBr0z21q7P8kXkjxr7lhVbRrv33SYS/4syU8nOW3e9vtJPj3++vY1HhkAgHVk6DuzyeixXFdX1eeTfC7JhUlOSHJVklTVNUnubK1d3FqbSfKl+RdX1V8mSWvtQccBADj2DR6zrbWPVNUjk7wlozd93ZLkea21uTeF7U5yaKj5AABYvwaP2SRprV2Z5MpFvnfWUa49dw1GAgCgA109zQAAAOYTswAAdEvMAgDQLTELAEC3xCwAAN0SswAAdGtdPJoLWD2ttczMzAw9BhMy/8/an/vGMjU15SPaIWIWjjkzMzM5++yzhx6DAezZs2foEZig6enpbNu2begxYHCWGQAA0C13ZuEYMzU1lenp6aHHYEJaa5mdnU2SbN261Y+dN5CpqamhR4B1QczCMaaq/Ohxgzn++OOHHgFgMJYZAADQLTELAEC3xCwAAN0SswAAdEvMAgDQLTELAEC3xCwAAN0SswAAdEvMAgDQLTELAEC3xCwAAN0SswAAdEvMAgDQrS1DDzCUAwcODD0CAACHsZxOq9baGo6y/lTVo5PcMfQcAAAc1a7W2p1HOmEjxmwlOSXJvUPPArBKtmf0f9J3xf+2AceO7Um+2Y4SqxsuZgGONVW1I8n+JDtba9ZQARuKN4ABANAtMQsAQLfELED/ZpNcNv4VYEOxZhYAgG65MwsAQLfELAAA3RKzAAB0S8wCANAtMQsAQLfELAAA3RKzAAB0S8wCANCt/w8MrWln4Dfp9gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#age distribution\n",
        "plt.figure(figsize = (8,5))\n",
        "plt.hist(new_customers['age'], bins=[0,24,44,64,100])\n",
        "plt.title('Age distribution histogram')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.savefig('age_dist_histogram.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "2AbLHN9dQCp5",
        "outputId": "265e6847-7554-4834-f82a-4a435c5ce2eb"
      },
      "execution_count": 487,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Conclusion**\n",
        "Upon analyzing the previous customer data it all these points can be concluded:\n",
        "* Females are higher in customer list but almost equal to men, doesn't really give us the information to target which gender in terms of marketing.\n",
        "* More customers are from Manufacturing, Financial and Health industries.\n",
        "* Mass Customers are more than affluent and high net worth customers, they can be targeted.\n",
        "* NSW customers are highest in number.\n",
        "* Solex brand is much popular.\n",
        "* Standard and Medium bicycles are much popular.\n",
        "* Age group 40-60 is much more interested in purchasing bicycles."
      ],
      "metadata": {
        "id": "cAQ2X5H5QPcu"
      }
    }
  ]
}