Week_2_Core_IP.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/DaveChui/Prep_Group-4-Project/blob/main/Week_2_Core_IP.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": [
        "The research problem is to figure out how we can predict which individuals are most likely to have or use a bank account."
      ],
      "metadata": {
        "id": "K3ZKLw_5qZBb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Defining the question"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "##a. Specifying the dat analytic questions\n",
        "\n",
        "Figure out how we can predict which individuals are most likely to have or use a bank account."
      ],
      "metadata": {
        "id": "r1H-FrFsyDpv"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "#b. Define the metrics for success\n",
        "\n",
        "####The project will entail the below steps:\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#####Exploratory Data Analysis\n",
        "#####Univariate, Bivariate and Multivariate analysis\n",
        "#####Visualizations\n",
        "#####Reduction techniques \n",
        "#####Mutiple Regression model"
      ],
      "metadata": {
        "id": "XIA6HjSzyDkr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#c) Understanding the context\n",
        "The solution will help provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania, and Uganda, while providing insights into some of the key demographic factors that might drive individuals’ financial outcomes."
      ],
      "metadata": {
        "id": "RVELN4FjyDdj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#d) Recording the Experimental Design\n",
        "#####1.Measure of central tendancy \n",
        "#####2.skewness\n",
        "#####3.variance \n",
        "#####4.univariate analysis \n",
        "#####5.bivariate analysis \n",
        "#####6.multivariate analysis"
      ],
      "metadata": {
        "id": "tJLGFJFDyDZK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#e) Data Relevance\n",
        "######The main dataset contains demographic information and what financial services are used by individuals across East Africa.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#####This data was extracted from various Finscope surveys ranging from 2016 to 2018, and more information about these surveys can be found here:\n",
        "#####FinAccess Kenya 2018: https://fsdkenya.org/publication/finaccess2019/\n",
        "#####Finscope Rwanda 2016: http://www.statistics.gov.rw/publication/finscope-rwanda-2016\n",
        "#####Finscope Tanzania 2017:  http://www.fsdt.or.tz/finscope/\n",
        "#####Finscope Uganda 2018: http://fsduganda.or.ug/finscope-2018-survey-report/ \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#####Dataset Files:\n",
        "#####Variable Definitions: http://bit.ly/VariableDefinitions \n",
        "#####Dataset: http://bit.ly/FinancialDataset"
      ],
      "metadata": {
        "id": "s74N3gAIyDSX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Loading the Data"
      ],
      "metadata": {
        "id": "wE9Qyku8yC95"
      }
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      "cell_type": "code",
      "execution_count": 25,
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        "id": "gmk-VWIVlL-N"
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      "source": [
        "#Import the required python libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data\n",
        "# ---\n",
        "FinancialDataset = pd.read_csv('FinancialDataset.csv', error_bad_lines=False)\n",
        "#Preview our data\n",
        "FinancialDataset.head(5)"
      ],
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              "  country  year    uniqueid Has a Bank account Type of Location  \\\n",
              "0   Kenya  2018  uniqueid_1                Yes            Rural   \n",
              "1   Kenya  2018  uniqueid_2                 No            Rural   \n",
              "2   Kenya  2018  uniqueid_3                Yes            Urban   \n",
              "3   Kenya  2018  uniqueid_4                 No            Rural   \n",
              "4   Kenya  2018  uniqueid_5                 No            Urban   \n",
              "\n",
              "  Cell Phone Access  household_size  Respondent Age gender_of_respondent  \\\n",
              "0               Yes             3.0            24.0               Female   \n",
              "1                No             5.0            70.0               Female   \n",
              "2               Yes             5.0            26.0                 Male   \n",
              "3               Yes             5.0            34.0               Female   \n",
              "4                No             8.0            26.0                 Male   \n",
              "\n",
              "  The relathip with head           marital_status  \\\n",
              "0                 Spouse  Married/Living together   \n",
              "1      Head of Household                  Widowed   \n",
              "2         Other relative     Single/Never Married   \n",
              "3      Head of Household  Married/Living together   \n",
              "4                  Child     Single/Never Married   \n",
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              "               Level of Educuation                Type of Job  \n",
              "0              Secondary education              Self employed  \n",
              "1              No formal education       Government Dependent  \n",
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              "      <td>Kenya</td>\n",
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              "      <td>uniqueid_5</td>\n",
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      "source": [
        "# 3. Checking the Data"
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      "source": [
        "#Check the number of records in the Dataset\n",
        "\n",
        "FinancialDataset.shape"
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      "source": [
        "# Preview the top of the dataset\n",
        "FinancialDataset.head()"
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            "text/plain": [
              "  country  year    uniqueid Has a Bank account Type of Location  \\\n",
              "0   Kenya  2018  uniqueid_1                Yes            Rural   \n",
              "1   Kenya  2018  uniqueid_2                 No            Rural   \n",
              "2   Kenya  2018  uniqueid_3                Yes            Urban   \n",
              "3   Kenya  2018  uniqueid_4                 No            Rural   \n",
              "4   Kenya  2018  uniqueid_5                 No            Urban   \n",
              "\n",
              "  Cell Phone Access  household_size  Respondent Age gender_of_respondent  \\\n",
              "0               Yes             3.0            24.0               Female   \n",
              "1                No             5.0            70.0               Female   \n",
              "2               Yes             5.0            26.0                 Male   \n",
              "3               Yes             5.0            34.0               Female   \n",
              "4                No             8.0            26.0                 Male   \n",
              "\n",
              "  The relathip with head           marital_status  \\\n",
              "0                 Spouse  Married/Living together   \n",
              "1      Head of Household                  Widowed   \n",
              "2         Other relative     Single/Never Married   \n",
              "3      Head of Household  Married/Living together   \n",
              "4                  Child     Single/Never Married   \n",
              "\n",
              "               Level of Educuation                Type of Job  \n",
              "0              Secondary education              Self employed  \n",
              "1              No formal education       Government Dependent  \n",
              "2  Vocational/Specialised training              Self employed  \n",
              "3                Primary education  Formally employed Private  \n",
              "4                Primary education        Informally employed  "
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              "      <th>1</th>\n",
              "      <td>Kenya</td>\n",
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              "      <th>2</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Other relative</td>\n",
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              "      <td>Vocational/Specialised training</td>\n",
              "      <td>Self employed</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_4</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Formally employed Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_5</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>No</td>\n",
              "      <td>8.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Informally employed</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-322a65a3-3db0-4500-bc3a-9a5c22e709e6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "      <script>\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-322a65a3-3db0-4500-bc3a-9a5c22e709e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview the bottom of the dataset\n",
        "FinancialDataset.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "NA0_VQC95-SV",
        "outputId": "b17bac9b-7361-4e2c-e832-39930f5aaa0f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      country  year       uniqueid Has a Bank account Type of Location  \\\n",
              "23519  Uganda  2018  uniqueid_2113                 No            Rural   \n",
              "23520  Uganda  2018  uniqueid_2114                 No            Rural   \n",
              "23521  Uganda  2018  uniqueid_2115                 No            Rural   \n",
              "23522  Uganda  2018  uniqueid_2116                 No            Urban   \n",
              "23523  Uganda  2018  uniqueid_2117                 No            Rural   \n",
              "\n",
              "      Cell Phone Access  household_size  Respondent Age gender_of_respondent  \\\n",
              "23519               Yes             4.0            48.0               Female   \n",
              "23520               Yes             2.0            27.0               Female   \n",
              "23521               Yes             5.0            27.0               Female   \n",
              "23522               Yes             7.0            30.0               Female   \n",
              "23523               Yes            10.0            20.0                 Male   \n",
              "\n",
              "      The relathip with head        marital_status  Level of Educuation  \\\n",
              "23519      Head of Household    Divorced/Seperated  No formal education   \n",
              "23520      Head of Household  Single/Never Married  Secondary education   \n",
              "23521                 Parent               Widowed    Primary education   \n",
              "23522                 Parent    Divorced/Seperated  Secondary education   \n",
              "23523                  Child  Single/Never Married  Secondary education   \n",
              "\n",
              "         Type of Job  \n",
              "23519   Other Income  \n",
              "23520   Other Income  \n",
              "23521   Other Income  \n",
              "23522  Self employed  \n",
              "23523      No Income  "
            ],
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              "      <td>Uganda</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>48.0</td>\n",
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              "      <td>Divorced/Seperated</td>\n",
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              "      <td>Uganda</td>\n",
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              "      <td>uniqueid_2116</td>\n",
              "      <td>No</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>Divorced/Seperated</td>\n",
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              "      <th>23523</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2117</td>\n",
              "      <td>No</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Secondary education</td>\n",
              "      <td>No Income</td>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1b204ac-11cb-47a4-bc1e-05b721f85602');\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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              "  "
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          },
          "metadata": {},
          "execution_count": 29
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for appropriate datatypes\n",
        "FinancialDataset.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6JcF9rK7EIb",
        "outputId": "ba7932fd-49ff-4374-a7df-97a810465c9a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country                    object\n",
              "year                        int64\n",
              "uniqueid                   object\n",
              "Has a Bank account         object\n",
              "Type of Location           object\n",
              "Cell Phone Access          object\n",
              "household_size            float64\n",
              "Respondent Age            float64\n",
              "gender_of_respondent       object\n",
              "The relathip with head     object\n",
              "marital_status             object\n",
              "Level of Educuation        object\n",
              "Type of Job                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for basic descriptive statistics for the numeric fields\n",
        "FinancialDataset.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JxALDNCFD1Im",
        "outputId": "1214841a-14b8-40c4-8919-e2eb48df2646"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               year  household_size  Respondent Age\n",
              "count  23524.000000    23496.000000    23490.000000\n",
              "mean    2016.979000        3.681818       38.804300\n",
              "std        0.899669        2.279933       16.519996\n",
              "min     2016.000000        0.000000       16.000000\n",
              "25%     2016.000000        2.000000       26.000000\n",
              "50%     2017.000000        3.000000       35.000000\n",
              "75%     2018.000000        5.000000       49.000000\n",
              "max     2056.000000       21.000000      100.000000"
            ],
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              "      <th></th>\n",
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              "      <th>count</th>\n",
              "      <td>23524.000000</td>\n",
              "      <td>23496.000000</td>\n",
              "      <td>23490.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016.979000</td>\n",
              "      <td>3.681818</td>\n",
              "      <td>38.804300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.899669</td>\n",
              "      <td>2.279933</td>\n",
              "      <td>16.519996</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2056.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8a7aa38-1f3e-4a2a-8359-36d3496ede74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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-d8a7aa38-1f3e-4a2a-8359-36d3496ede74 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-d8a7aa38-1f3e-4a2a-8359-36d3496ede74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the fields in the dataset\n",
        "\n",
        "FinancialDataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1AESum79Hc8",
        "outputId": "6eede8f8-ed2d-4bb0-bea0-3e1f02ed6922"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'year', 'uniqueid', 'Has a Bank account', 'Type of Location',\n",
              "       'Cell Phone Access', 'household_size', 'Respondent Age',\n",
              "       'gender_of_respondent', 'The relathip with head', 'marital_status',\n",
              "       'Level of Educuation', 'Type of Job'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Data Validation\n",
        "According to the external data source from Financal Sector Deepening(FSD), the dataset is valid apart from the fictional years captured. e.g 2056, 2029"
      ],
      "metadata": {
        "id": "Jj5EHFJ3_xWZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Data Cleaning"
      ],
      "metadata": {
        "id": "i0c7aGx0_nLY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop uniqueid column which is not required in the analysis\n",
        "\n",
        "FinancialDataset = FinancialDataset.drop(['uniqueid'], axis = 1)"
      ],
      "metadata": {
        "id": "OsxjrXGu_q9H"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Format field names to lower case and remove spaces\n",
        "\n",
        "FinancialDataset.columns = FinancialDataset.columns.str.lower().str.replace(\" \", \"_\")"
      ],
      "metadata": {
        "id": "7dmfz0ENByi-"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Correct wrong field names i.e \"level_of_educuation\" and \"the_relathip_with_head\"\n",
        "\n",
        "FinancialDataset.rename(columns={'the_relathip_with_head':'the_relationship_with_head'}, inplace=True)\n",
        "FinancialDataset.rename(columns={'level_of_educuation':'level_of_education'}, inplace=True)"
      ],
      "metadata": {
        "id": "XohvxZjjCNaZ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Confirm the field names\n",
        "\n",
        "FinancialDataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1stu6RVJDG7I",
        "outputId": "07d1cfbd-65f4-440c-bc8a-8ae24f70e004"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'year', 'has_a_bank_account', 'type_of_location',\n",
              "       'cell_phone_access', 'household_size', 'respondent_age',\n",
              "       'gender_of_respondent', 'the_relationship_with_head', 'marital_status',\n",
              "       'level_of_education', 'type_of_job'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for missing values per field\n",
        "\n",
        "FinancialDataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4l73R0jeDLW6",
        "outputId": "ae4f6a72-1c7f-4b50-8852-38429f5a2b0f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country                       14\n",
              "year                           0\n",
              "has_a_bank_account            36\n",
              "type_of_location              15\n",
              "cell_phone_access             11\n",
              "household_size                28\n",
              "respondent_age                34\n",
              "gender_of_respondent          34\n",
              "the_relationship_with_head     4\n",
              "marital_status                32\n",
              "level_of_education            29\n",
              "type_of_job                   30\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop the null values\n",
        "\n",
        "FinancialDataset.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "5r7Qc5RgDcQ6"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Confirm that there are no missing values\n",
        "\n",
        "FinancialDataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWqnGSM6EFL9",
        "outputId": "c6561ea6-15b0-4902-a8b3-3336b5cf1d3d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country                       0\n",
              "year                          0\n",
              "has_a_bank_account            0\n",
              "type_of_location              0\n",
              "cell_phone_access             0\n",
              "household_size                0\n",
              "respondent_age                0\n",
              "gender_of_respondent          0\n",
              "the_relationship_with_head    0\n",
              "marital_status                0\n",
              "level_of_education            0\n",
              "type_of_job                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for outliers in household_size of respondent_age \n",
        "\n",
        "plt.figure(dpi = 100)\n",
        "FinancialDataset.boxplot(['household_size'])\n",
        "plt.title(\"A boxplot to show household size\")\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "w9Vmki6aEI8d",
        "outputId": "dfda557b-977d-441f-8211-f6b1ad438259"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing outliers in household size column\n",
        "lowscore,highscore = remove_outlier(FinancialDataset['household_size'])\n",
        "FinancialDataset['household_size']=np.where(FinancialDataset['household_size']>highscore,highscore,FinancialDataset['household_size'])\n",
        "FinancialDataset['household_size']=np.where(FinancialDataset['household_size']<lowscore,lowscore,FinancialDataset['household_size'])"
      ],
      "metadata": {
        "id": "p7fP0uVXG6ko"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(dpi = 100)\n",
        "FinancialDataset.boxplot(['respondent_age'])\n",
        "plt.title(\"A boxplot to show respondent ages\")\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "ZDLEXy9EFfoH",
        "outputId": "0af31a1e-b639-4803-b24b-fb2c73be8ba7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove outliers for Respondent age column\n",
        "def remove_outlier(col):\n",
        "  sorted(col)\n",
        "  Q1,Q3=col.quantile([0.25,0.75])\n",
        "  IQR=Q3-Q1\n",
        "  Lower_range= Q1-(1.5 * IQR)\n",
        "  Upper_range= Q3+(1.5 * IQR)\n",
        "  return Lower_range, Upper_range"
      ],
      "metadata": {
        "id": "BX-8zi1jGvch"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(dpi = 100)\n",
        "FinancialDataset.boxplot(['year'])\n",
        "plt.title(\"A boxplot showing years\")\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "Sl27RLWWF172",
        "outputId": "2912399b-bba1-4a3d-fd1b-d42a7301cb96"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Remove outliers in the year field\n",
        "outliers = FinancialDataset[FinancialDataset['year'] > 2018].index\n",
        "FinancialDataset.drop(outliers , inplace=True)\n",
        "FinancialDataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Vc_7im5GF97",
        "outputId": "9006e105-0799-4fcb-ee85-d01e022cc794"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23340, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FinancialDataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYeCQNvaIEP2",
        "outputId": "6b385493-c0cd-4bb9-ea91-f8a38bd864f5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 23340 entries, 0 to 23523\n",
            "Data columns (total 12 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   country                     23340 non-null  object \n",
            " 1   year                        23340 non-null  int64  \n",
            " 2   has_a_bank_account          23340 non-null  object \n",
            " 3   type_of_location            23340 non-null  object \n",
            " 4   cell_phone_access           23340 non-null  object \n",
            " 5   household_size              23340 non-null  float64\n",
            " 6   respondent_age              23340 non-null  float64\n",
            " 7   gender_of_respondent        23340 non-null  object \n",
            " 8   the_relationship_with_head  23340 non-null  object \n",
            " 9   marital_status              23340 non-null  object \n",
            " 10  level_of_education          23340 non-null  object \n",
            " 11  type_of_job                 23340 non-null  object \n",
            "dtypes: float64(2), int64(1), object(9)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping the rows containing the above years.\n",
        "\n",
        "FinancialDataset.drop(FinancialDataset[FinancialDataset.year.isin([2029, 2056, 2039])].index, inplace=True)"
      ],
      "metadata": {
        "id": "NecRV5a2IKnq"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FinancialDataset.year.unique()\n",
        "#array([2018, 2029, 2056, 2016, 2039, 2017])\n",
        "\n",
        "#year 2029, 2056, 2039 aren't in our scope hence they've been dropped"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JK1vPMSpLJqG",
        "outputId": "dba10180-8abd-4ca6-ba47-80e074cbc5ba"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2018, 2016, 2017])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FinancialDataset.year.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RK_zliGILhX4",
        "outputId": "5c544196-f468-4cf5-acf6-3906df05740d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016    8683\n",
              "2018    8157\n",
              "2017    6500\n",
              "Name: year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the 'Year' column to datetime format\n",
        "FinancialDataset['year']= pd.to_datetime(FinancialDataset['year'], format='%Y')"
      ],
      "metadata": {
        "id": "FS2vFLuwL3-7"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FinancialDataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMydMtHqMOBf",
        "outputId": "3172bdb9-dfa1-4b57-fb71-b0a3f8bc387c"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 23340 entries, 0 to 23523\n",
            "Data columns (total 12 columns):\n",
            " #   Column                      Non-Null Count  Dtype         \n",
            "---  ------                      --------------  -----         \n",
            " 0   country                     23340 non-null  object        \n",
            " 1   year                        23340 non-null  datetime64[ns]\n",
            " 2   has_a_bank_account          23340 non-null  object        \n",
            " 3   type_of_location            23340 non-null  object        \n",
            " 4   cell_phone_access           23340 non-null  object        \n",
            " 5   household_size              23340 non-null  float64       \n",
            " 6   respondent_age              23340 non-null  float64       \n",
            " 7   gender_of_respondent        23340 non-null  object        \n",
            " 8   the_relationship_with_head  23340 non-null  object        \n",
            " 9   marital_status              23340 non-null  object        \n",
            " 10  level_of_education          23340 non-null  object        \n",
            " 11  type_of_job                 23340 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), object(9)\n",
            "memory usage: 2.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. Exploratory Analysis\n",
        "\n",
        "Univariate Analysis"
      ],
      "metadata": {
        "id": "06m7VTIMNhmH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#sumarry statistics\n",
        "FinancialDataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4nmccNC1OPrR",
        "outputId": "44a99d53-fdee-4ebe-e100-c8c20b81ddd2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       household_size  respondent_age\n",
              "count    23340.000000    23340.000000\n",
              "mean         3.662339       38.807069\n",
              "std          2.194161       16.519905\n",
              "min          0.000000       16.000000\n",
              "25%          2.000000       26.000000\n",
              "50%          3.000000       35.000000\n",
              "75%          5.000000       49.000000\n",
              "max          9.500000      100.000000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>household_size</th>\n",
              "      <th>respondent_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23340.000000</td>\n",
              "      <td>23340.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.662339</td>\n",
              "      <td>38.807069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.194161</td>\n",
              "      <td>16.519905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.500000</td>\n",
              "      <td>100.000000</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bar Graphs"
      ],
      "metadata": {
        "id": "pRhZ0iD3PPlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A bar graph of the respondents per country \n",
        "\n",
        "# specify the chart size\n",
        "plt.figure(dpi = 100)\n",
        "sns.set_style('darkgrid')\n",
        "sns.countplot(FinancialDataset.country)\n",
        "\n",
        "# Give a title and font color to our chart \n",
        "plt.title('A bar Plot to show the number of Respondents per Country', color = 'black')\n",
        "\n",
        "# Display the chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "UNPcFGatO8wx",
        "outputId": "5e28d199-d35f-4924-a7b0-cefe9d0f0a68"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A horizontal bar chart of the respondents' highest level of education \n",
        "\n",
        "# grouping the data to be plotted\n",
        "study = FinancialDataset.level_of_education.value_counts()\n",
        "study\n",
        "\n",
        "# accessing the data to plot\n",
        "\n",
        "# labels\n",
        "size3= study.index\n",
        "size3\n",
        "\n",
        "# values\n",
        "data3 = study.values\n",
        "data3\n",
        "\n",
        "# specifying our chart size\n",
        "plt.figure(figsize = (8,8), dpi = 125)\n",
        "plt.barh(size3, data3, color = 'green')\n",
        "\n",
        "# labeling the y axis\n",
        "plt.ylabel(\"Highest level of education\")\n",
        "\n",
        "# labeling the x axis\n",
        "plt.xlabel('Frequency')\n",
        "\n",
        "# Giving a title to our chart\n",
        "plt.title(\"A bar chart of respondents' level of education\", color = 'black')\n",
        "\n",
        "# Displaying our chart\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# most respondents' highest level of education was primary school\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "RgmUybBKPKwF",
        "outputId": "3952d234-2969-4122-d608-1a3787799221"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(FinancialDataset.type_of_location, hue=FinancialDataset.country)\n",
        "plt.show();\n",
        "\n",
        "#The graph below shows rural/urban areas in the different countries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "gBZCnf2rQMoi",
        "outputId": "53daa41a-d78b-4d80-9bdc-be66c248d5b4"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate measures of central tendency\n",
        "FinancialDataset.mean()\n",
        "print(FinancialDataset.mean())\n",
        "FinancialDataset.mode()\n",
        "print(FinancialDataset.mode())\n",
        "FinancialDataset.median()\n",
        "print(FinancialDataset.median())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9a0HeGCRBhK",
        "outputId": "df138333-ee35-4717-a35f-830aca4b72ed"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "household_size     3.662339\n",
            "respondent_age    38.807069\n",
            "dtype: float64\n",
            "  country       year has_a_bank_account type_of_location cell_phone_access  \\\n",
            "0  Rwanda 2016-01-01                 No            Rural               Yes   \n",
            "\n",
            "   household_size  respondent_age gender_of_respondent  \\\n",
            "0             2.0            30.0               Female   \n",
            "\n",
            "  the_relationship_with_head           marital_status level_of_education  \\\n",
            "0          Head of Household  Married/Living together  Primary education   \n",
            "\n",
            "     type_of_job  \n",
            "0  Self employed  \n",
            "household_size     3.0\n",
            "respondent_age    35.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  import sys\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting a age histogram\n",
        "age_points = FinancialDataset['respondent_age']\n",
        "\n",
        "plt.hist(age_points, bins=10, histtype='bar', rwidth=0.9)\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Histogram of age against frequency')\n",
        "plt.show()\n",
        "#We can deduce that the greater population in the dataset is between the ages\n",
        "# of 25 and 30."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "V_CMVz4DU1O6",
        "outputId": "5b1ebebe-100a-4d8a-a529-47f23f77bf2c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting a age histogram\n",
        "size_points = FinancialDataset['household_size']\n",
        "\n",
        "plt.hist(size_points, bins=8, histtype='bar', rwidth=0.9)\n",
        "plt.xlabel('Household sizes')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Histogram of Household_sizes vs frequency')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "dLgAdccfVGPv",
        "outputId": "5d9516f0-06d9-439e-c665-d7482f43abd5"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min, max and range for age and household size\n",
        "#age\n",
        "min_age = FinancialDataset['respondent_age'].min()\n",
        "print(min_age)\n",
        "max_age = FinancialDataset['respondent_age'].max()\n",
        "print(max_age)\n",
        "range_age = max_age - min_age\n",
        "print(range_age)\n",
        "#For household size\n",
        "min_size = FinancialDataset['household_size'].min()\n",
        "print(min_size)\n",
        "max_size = FinancialDataset['household_size'].max()\n",
        "print(max_size)\n",
        "range_size = max_size - min_size\n",
        "print(range_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOojzsLMRxG5",
        "outputId": "b88c8067-1850-4c89-cee5-f6610e53af77"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16.0\n",
            "100.0\n",
            "84.0\n",
            "0.0\n",
            "9.5\n",
            "9.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate quantiles\n",
        "quantile_age = FinancialDataset[\"respondent_age\"].quantile([0.25,0.5,0.75])\n",
        "print(quantile_age)\n",
        "quantile_size = FinancialDataset[\"household_size\"].quantile([0.25,0.5,0.75])\n",
        "print(quantile_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrwcyJ1TTIVQ",
        "outputId": "30d30fdd-10e5-425b-cef0-0759fce39bdb"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.25    26.0\n",
            "0.50    35.0\n",
            "0.75    49.0\n",
            "Name: respondent_age, dtype: float64\n",
            "0.25    2.0\n",
            "0.50    3.0\n",
            "0.75    5.0\n",
            "Name: household_size, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate measures of dispersion\n",
        "st_dev = FinancialDataset.std()\n",
        "print(st_dev)\n",
        "skew = FinancialDataset.skew()\n",
        "print(skew)\n",
        "kurt = FinancialDataset.kurtosis()\n",
        "print(kurt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJjvUdFwTY_n",
        "outputId": "7042f8d0-819d-4f24-f2cd-6bcc5a828bd6"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "year              310 days 08:42:26.936346008\n",
            "household_size                       2.194161\n",
            "respondent_age                      16.519905\n",
            "dtype: object\n",
            "household_size    0.700081\n",
            "respondent_age    0.840615\n",
            "dtype: float64\n",
            "household_size   -0.283137\n",
            "respondent_age    0.098268\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Summary \n",
        "1. The mean household size is 3.61, Rwanda had the highest number of entries, the data for age was positively skewed at 0.44 meaning data is approximately skewed and kurtosis of -0.83. \n",
        "2.  The mean age is 40.25, Rwanda had the highest number of entries, the data for age was positively skewed at 0.75 meaning data is moderately skewed and has a kurtosis of -0.16. \n"
      ],
      "metadata": {
        "id": "fIbdOmSJTxB8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#7. Bivariate Analysis\n",
        "\n",
        "We will perform Bivariate analysis, check relationships and differences between two variables. We will start with Numeric-Numeric variables, then Categorical-Categorical and lastly Categorical-Numeric variables"
      ],
      "metadata": {
        "id": "DG0T_fsDUb3A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Numeric-Numeric Variables\n",
        "#Check for numeric variables in our data\n",
        "#isolate numerical datatypes\n",
        "numerical = FinancialDataset.select_dtypes(include=['int64','float64','Int64'])[:]\n",
        "numerical.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbrE0rqmUuw3",
        "outputId": "8b83b4c3-6378-4f17-fcb3-a449ebd37f46"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "household_size    float64\n",
              "respondent_age    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ploting the bivariate summaries and recording our observations\n",
        "sns.pairplot(FinancialDataset)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "W2HjebHDqJ_Q",
        "outputId": "78eba2b9-f5ee-4dc8-b30f-406def0d960e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting a scatterplot\n",
        "\n",
        "plt.scatter(FinancialDataset['household_size'], FinancialDataset['respondent_age'])\n",
        "\n",
        "# Giving our scatterplot a title\n",
        "plt.title('Respondent age against household size')\n",
        "\n",
        "# Labelling our x axis\n",
        "plt.xlabel('Household size')\n",
        "\n",
        "# Labelling our y axis\n",
        "plt.ylabel('Respondent Age')\n",
        "\n",
        "# Displaying our scatterplot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "R7Goj1JZqXWW",
        "outputId": "e3f88877-b1fe-4d54-b92f-b9a7a1fe6dd5"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the pearson correlation coefficient\n",
        "pearson_coeff = FinancialDataset['household_size'].corr(FinancialDataset['respondent_age'], method=\"pearson\") \n",
        "print(pearson_coeff)\n",
        "#There's a significant negative correlation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEUR8mivqsBe",
        "outputId": "b5ab4e37-c566-4005-e94b-e01ef6b4f228"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.12191002095718263\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the data count\n",
        "FinancialDataset.type_of_job.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISk4P4kMrYFe",
        "outputId": "eb211d4d-2c34-46cd-bd28-e79bb12c96c9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Self employed                   6361\n",
              "Informally employed             5549\n",
              "Farming and Fishing             5422\n",
              "Remittance Dependent            2508\n",
              "Other Income                    1077\n",
              "Formally employed Private       1047\n",
              "No Income                        618\n",
              "Formally employed Government     386\n",
              "Government Dependent             247\n",
              "Dont Know/Refuse to answer       125\n",
              "Name: type_of_job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate the correlation\n",
        "correlation = numerical.dropna().corr()\n",
        "correlation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "_91KsCZYspiE",
        "outputId": "ab7273d5-b960-4f54-a959-590c42c60f31"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                household_size  respondent_age\n",
              "household_size         1.00000        -0.12191\n",
              "respondent_age        -0.12191         1.00000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c61f3a0-2def-40c6-ae4a-fa97e58b7d29\">\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>household_size</th>\n",
              "      <th>respondent_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>household_size</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.12191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>respondent_age</th>\n",
              "      <td>-0.12191</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c61f3a0-2def-40c6-ae4a-fa97e58b7d29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7c61f3a0-2def-40c6-ae4a-fa97e58b7d29 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-7c61f3a0-2def-40c6-ae4a-fa97e58b7d29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot a correlation heatmap\n",
        "sns.heatmap(correlation, xticklabels=correlation.columns, yticklabels=correlation.columns, annot=True)\n",
        "#There is basically no correlation between household size and age of respondents"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "x3vwBd0Az1FB",
        "outputId": "93291446-0873-4021-e7bc-03623ddf000c"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b79651e90>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check Which genger is most likely to have a bank account\n",
        "ax1 = FinancialDataset.groupby('has_a_bank_account')['gender_of_respondent'].value_counts(normalize=True).unstack()\n",
        "ax1.plot(kind='bar', stacked='True',title=str(ax1))\n",
        "int_level = FinancialDataset['has_a_bank_account'].value_counts()\n",
        "#we can deduce that the difference between the male and female individuals who own a bank account is \n",
        "#quite evident. Males have a higher number."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "pcuthLjW2utF",
        "outputId": "a761e40f-8636-45d5-8460-2347fac39c3b"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From which working/non-working group does an account owner belong to?\n",
        "\n",
        "ax2 = FinancialDataset.groupby('has_a_bank_account')['type_of_job'].value_counts(normalize=True).unstack()\n",
        "ax2.plot(kind='bar', stacked='True',title=str(ax1))\n",
        "int_level = FinancialDataset['has_a_bank_account'].value_counts()\n",
        "#Individuals who are self employed are most likely to have a bank account and\n",
        "# those who are informally employed are less likely to have a bank account\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "c0zF0bVz8qE-",
        "outputId": "058e0064-c047-4fa9-80ae-e675b6791af6"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary**\n",
        "\n",
        "1.   The number of Males who own a bank account is significantly more than females.\n",
        "2.   Individuals who are self employed are most likely to have a bank account and those who are informally employed are less likely to have a bank account\n",
        "3. Individuals from urban setup are most likely to have a bank account and\n",
        "those from rural areas are less likely to have a bank account\n",
        "4.  Individuals with a secondary education are most likely to have a bank account and those with a primary level education are less likely to have a bank account\n",
        "5. Individuals from Kenya are most likely to have a bank account and those Uganda are less likely to have a bank account\n",
        "6. Individuals between the ages of 30-50, adults are most likely to have a bank account and those above 80years are less likely to have a bank account\n",
        "\n"
      ],
      "metadata": {
        "id": "vpkWEUOxQf6r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multivariate Analysis \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Perform the Linear Discriminant Analysis Reduction Technique and conduct the multiple regression analysis"
      ],
      "metadata": {
        "id": "L2Gv1BD8RJw0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The focus is on analyzing more than 2 statistical variables at once. We have many variables. To visualize patterns much better, we can reduce the variables into 2 or 3 significant ones without losing the essence of the data collected.\n",
        "\n",
        "To analyse multiple categorical variables we can turn them into dummies (zeros and ones). Pandas can turn a categorical variables into a series of zeros and ones, making them easier to compare."
      ],
      "metadata": {
        "id": "O7Pn9bNllSef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for a relationship between dependent and independent variable\n",
        "df= FinancialDataset.copy()\n",
        "df.head(3)\n",
        "to_nums = {\"has_a_bank_account\": {\"Yes\": 1, \"No\": 0},\n",
        "           \"type_of_location\": {\"Rural\": 0, \"Urban\": 1},\n",
        "           \"gender_of_respondent\": {\"Female\": 2, \"Male\": 3}}\n",
        "import scipy.stats as stats\n",
        "\n",
        "#To convert the columns to numbers using replace :\n",
        "FinancialDataset.replace(to_nums, inplace=True)\n",
        "print(FinancialDataset.head())\n",
        "print(FinancialDataset.info())\n",
        "#calculate point-biserial correlation\n",
        "x = FinancialDataset['has_a_bank_account']\n",
        "y = FinancialDataset['respondent_age']\n",
        "stats.pointbiserialr(x, y)\n",
        "#There is close to no linear correlation between age and having a bank account\n",
        "\n",
        "x = FinancialDataset['has_a_bank_account']\n",
        "y = FinancialDataset['household_size']\n",
        "stats.pointbiserialr(x, y)\n",
        "#There is no linear correlation between householdd size and having a bank account\n",
        "\n",
        "x = FinancialDataset['has_a_bank_account']\n",
        "y = FinancialDataset['type_of_location']\n",
        "stats.pointbiserialr(x, y)\n",
        "#There is no linear correlation between householdd size and having a bank account\n",
        "\n",
        "x = FinancialDataset['has_a_bank_account']\n",
        "y = FinancialDataset['gender_of_respondent']\n",
        "stats.pointbiserialr(x, y)\n",
        "#There is a weak correlation between gender and having a bank account"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZS9Wnla4PihI",
        "outputId": "32d05c06-001e-45dd-fd78-eba6c717b9d0"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  country       year  has_a_bank_account  type_of_location cell_phone_access  \\\n",
            "0   Kenya 2018-01-01                   1                 0               Yes   \n",
            "1   Kenya 2018-01-01                   0                 0                No   \n",
            "2   Kenya 2018-01-01                   1                 1               Yes   \n",
            "3   Kenya 2018-01-01                   0                 0               Yes   \n",
            "4   Kenya 2018-01-01                   0                 1                No   \n",
            "\n",
            "   household_size  respondent_age  gender_of_respondent  \\\n",
            "0             3.0            24.0                     2   \n",
            "1             5.0            70.0                     2   \n",
            "2             5.0            26.0                     3   \n",
            "3             5.0            34.0                     2   \n",
            "4             8.0            26.0                     3   \n",
            "\n",
            "  the_relationship_with_head           marital_status  \\\n",
            "0                     Spouse  Married/Living together   \n",
            "1          Head of Household                  Widowed   \n",
            "2             Other relative     Single/Never Married   \n",
            "3          Head of Household  Married/Living together   \n",
            "4                      Child     Single/Never Married   \n",
            "\n",
            "                level_of_education                type_of_job  \n",
            "0              Secondary education              Self employed  \n",
            "1              No formal education       Government Dependent  \n",
            "2  Vocational/Specialised training              Self employed  \n",
            "3                Primary education  Formally employed Private  \n",
            "4                Primary education        Informally employed  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 23340 entries, 0 to 23523\n",
            "Data columns (total 12 columns):\n",
            " #   Column                      Non-Null Count  Dtype         \n",
            "---  ------                      --------------  -----         \n",
            " 0   country                     23340 non-null  object        \n",
            " 1   year                        23340 non-null  datetime64[ns]\n",
            " 2   has_a_bank_account          23340 non-null  int64         \n",
            " 3   type_of_location            23340 non-null  int64         \n",
            " 4   cell_phone_access           23340 non-null  object        \n",
            " 5   household_size              23340 non-null  float64       \n",
            " 6   respondent_age              23340 non-null  float64       \n",
            " 7   gender_of_respondent        23340 non-null  int64         \n",
            " 8   the_relationship_with_head  23340 non-null  object        \n",
            " 9   marital_status              23340 non-null  object        \n",
            " 10  level_of_education          23340 non-null  object        \n",
            " 11  type_of_job                 23340 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(3), object(6)\n",
            "memory usage: 2.8+ MB\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PointbiserialrResult(correlation=0.11605756558778187, pvalue=8.476627554148982e-71)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert categorical variables into dummy variables with digits -  zeros and ones\n",
        "# pd.get_dummies creates a new dataframe which consists of zeros and ones.\n",
        "\n",
        "dummy = pd.get_dummies(FinancialDataset[['country', 'type_of_location', 'cell_phone_access', 'gender_of_respondent','the_relationship_with_head', 'marital_status', 'level_of_education', 'type_of_job']])\n",
        "dummy.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "mQza36e6mHaF",
        "outputId": "74e3b733-9f74-4404-ae9e-e3e0c78b6774"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type_of_location  gender_of_respondent  country_Kenya  country_Rwanda  \\\n",
              "0                 0                     2              1               0   \n",
              "1                 0                     2              1               0   \n",
              "2                 1                     3              1               0   \n",
              "3                 0                     2              1               0   \n",
              "4                 1                     3              1               0   \n",
              "\n",
              "   country_Tanzania  country_Uganda  cell_phone_access_No  \\\n",
              "0                 0               0                     0   \n",
              "1                 0               0                     1   \n",
              "2                 0               0                     0   \n",
              "3                 0               0                     0   \n",
              "4                 0               0                     1   \n",
              "\n",
              "   cell_phone_access_Yes  the_relationship_with_head_Child  \\\n",
              "0                      1                                 0   \n",
              "1                      0                                 0   \n",
              "2                      1                                 0   \n",
              "3                      1                                 0   \n",
              "4                      0                                 1   \n",
              "\n",
              "   the_relationship_with_head_Head of Household  ...  \\\n",
              "0                                             0  ...   \n",
              "1                                             1  ...   \n",
              "2                                             0  ...   \n",
              "3                                             1  ...   \n",
              "4                                             0  ...   \n",
              "\n",
              "   type_of_job_Dont Know/Refuse to answer  type_of_job_Farming and Fishing  \\\n",
              "0                                       0                                0   \n",
              "1                                       0                                0   \n",
              "2                                       0                                0   \n",
              "3                                       0                                0   \n",
              "4                                       0                                0   \n",
              "\n",
              "   type_of_job_Formally employed Government  \\\n",
              "0                                         0   \n",
              "1                                         0   \n",
              "2                                         0   \n",
              "3                                         0   \n",
              "4                                         0   \n",
              "\n",
              "   type_of_job_Formally employed Private  type_of_job_Government Dependent  \\\n",
              "0                                      0                                 0   \n",
              "1                                      0                                 1   \n",
              "2                                      0                                 0   \n",
              "3                                      1                                 0   \n",
              "4                                      0                                 0   \n",
              "\n",
              "   type_of_job_Informally employed  type_of_job_No Income  \\\n",
              "0                                0                      0   \n",
              "1                                0                      0   \n",
              "2                                0                      0   \n",
              "3                                0                      0   \n",
              "4                                1                      0   \n",
              "\n",
              "   type_of_job_Other Income  type_of_job_Remittance Dependent  \\\n",
              "0                         0                                 0   \n",
              "1                         0                                 0   \n",
              "2                         0                                 0   \n",
              "3                         0                                 0   \n",
              "4                         0                                 0   \n",
              "\n",
              "   type_of_job_Self employed  \n",
              "0                          1  \n",
              "1                          0  \n",
              "2                          1  \n",
              "3                          0  \n",
              "4                          0  \n",
              "\n",
              "[5 rows x 36 columns]"
            ],
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We have created new dataframe. To compare it to our original dataframe, we\n",
        "# need to concatenate them to work with them appropriately.\n",
        "\n",
        "# The dummy variables will be on the right side of the dataframe.\n",
        "# As they are columns, we concatenate them on axis=1.\n",
        "\n",
        "finance2 = pd.concat([FinancialDataset, dummy], axis = 1)\n",
        "finance2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "Zm1FFP4VnFLv",
        "outputId": "ec6075d2-45ec-4df1-fafe-5d595f4ce99f"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  country       year  has_a_bank_account  type_of_location cell_phone_access  \\\n",
              "0   Kenya 2018-01-01                   1                 0               Yes   \n",
              "1   Kenya 2018-01-01                   0                 0                No   \n",
              "2   Kenya 2018-01-01                   1                 1               Yes   \n",
              "3   Kenya 2018-01-01                   0                 0               Yes   \n",
              "4   Kenya 2018-01-01                   0                 1                No   \n",
              "\n",
              "   household_size  respondent_age  gender_of_respondent  \\\n",
              "0             3.0            24.0                     2   \n",
              "1             5.0            70.0                     2   \n",
              "2             5.0            26.0                     3   \n",
              "3             5.0            34.0                     2   \n",
              "4             8.0            26.0                     3   \n",
              "\n",
              "  the_relationship_with_head           marital_status  ...  \\\n",
              "0                     Spouse  Married/Living together  ...   \n",
              "1          Head of Household                  Widowed  ...   \n",
              "2             Other relative     Single/Never Married  ...   \n",
              "3          Head of Household  Married/Living together  ...   \n",
              "4                      Child     Single/Never Married  ...   \n",
              "\n",
              "  type_of_job_Dont Know/Refuse to answer type_of_job_Farming and Fishing  \\\n",
              "0                                      0                               0   \n",
              "1                                      0                               0   \n",
              "2                                      0                               0   \n",
              "3                                      0                               0   \n",
              "4                                      0                               0   \n",
              "\n",
              "   type_of_job_Formally employed Government  \\\n",
              "0                                         0   \n",
              "1                                         0   \n",
              "2                                         0   \n",
              "3                                         0   \n",
              "4                                         0   \n",
              "\n",
              "   type_of_job_Formally employed Private  type_of_job_Government Dependent  \\\n",
              "0                                      0                                 0   \n",
              "1                                      0                                 1   \n",
              "2                                      0                                 0   \n",
              "3                                      1                                 0   \n",
              "4                                      0                                 0   \n",
              "\n",
              "   type_of_job_Informally employed  type_of_job_No Income  \\\n",
              "0                                0                      0   \n",
              "1                                0                      0   \n",
              "2                                0                      0   \n",
              "3                                0                      0   \n",
              "4                                1                      0   \n",
              "\n",
              "   type_of_job_Other Income  type_of_job_Remittance Dependent  \\\n",
              "0                         0                                 0   \n",
              "1                         0                                 0   \n",
              "2                         0                                 0   \n",
              "3                         0                                 0   \n",
              "4                         0                                 0   \n",
              "\n",
              "   type_of_job_Self employed  \n",
              "0                          1  \n",
              "1                          0  \n",
              "2                          1  \n",
              "3                          0  \n",
              "4                          0  \n",
              "\n",
              "[5 rows x 48 columns]"
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              "      <th>type_of_job_Farming and Fishing</th>\n",
              "      <th>type_of_job_Formally employed Government</th>\n",
              "      <th>type_of_job_Formally employed Private</th>\n",
              "      <th>type_of_job_Government Dependent</th>\n",
              "      <th>type_of_job_Informally employed</th>\n",
              "      <th>type_of_job_No Income</th>\n",
              "      <th>type_of_job_Other Income</th>\n",
              "      <th>type_of_job_Remittance Dependent</th>\n",
              "      <th>type_of_job_Self employed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>24.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>5.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Widowed</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Other relative</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>8.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-036db2ef-7dd8-4999-ba2c-780629ed5a5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing steps\n",
        "\n",
        "# The first step is to divide the dataset into;\n",
        "# a feature set: X, \n",
        "# and corresponding label: y.\n",
        "\n",
        "\n",
        "X = finance2.drop(columns = ['country', 'has_a_bank_account',\t'type_of_location',\t'cell_phone_access', 'household_size',\t'respondent_age',\t'gender_of_respondent',\t'the_relationship_with_head',\t'marital_status',\t'level_of_education' ,\t'type_of_job'], axis = 1)\n",
        "y = finance2['has_a_bank_account']"
      ],
      "metadata": {
        "id": "4ZWkRA7MOqL4"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "be6rr7-5Q7ax",
        "outputId": "ca38a965-c2bc-4e21-d0d7-5c3a6e331908"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        year  country_Kenya  country_Rwanda  country_Tanzania  country_Uganda  \\\n",
              "0 2018-01-01              1               0                 0               0   \n",
              "1 2018-01-01              1               0                 0               0   \n",
              "2 2018-01-01              1               0                 0               0   \n",
              "3 2018-01-01              1               0                 0               0   \n",
              "4 2018-01-01              1               0                 0               0   \n",
              "\n",
              "   cell_phone_access_No  cell_phone_access_Yes  \\\n",
              "0                     0                      1   \n",
              "1                     1                      0   \n",
              "2                     0                      1   \n",
              "3                     0                      1   \n",
              "4                     1                      0   \n",
              "\n",
              "   the_relationship_with_head_Child  \\\n",
              "0                                 0   \n",
              "1                                 0   \n",
              "2                                 0   \n",
              "3                                 0   \n",
              "4                                 1   \n",
              "\n",
              "   the_relationship_with_head_Head of Household  \\\n",
              "0                                             0   \n",
              "1                                             1   \n",
              "2                                             0   \n",
              "3                                             1   \n",
              "4                                             0   \n",
              "\n",
              "   the_relationship_with_head_Other non-relatives  ...  \\\n",
              "0                                               0  ...   \n",
              "1                                               0  ...   \n",
              "2                                               0  ...   \n",
              "3                                               0  ...   \n",
              "4                                               0  ...   \n",
              "\n",
              "   type_of_job_Dont Know/Refuse to answer  type_of_job_Farming and Fishing  \\\n",
              "0                                       0                                0   \n",
              "1                                       0                                0   \n",
              "2                                       0                                0   \n",
              "3                                       0                                0   \n",
              "4                                       0                                0   \n",
              "\n",
              "   type_of_job_Formally employed Government  \\\n",
              "0                                         0   \n",
              "1                                         0   \n",
              "2                                         0   \n",
              "3                                         0   \n",
              "4                                         0   \n",
              "\n",
              "   type_of_job_Formally employed Private  type_of_job_Government Dependent  \\\n",
              "0                                      0                                 0   \n",
              "1                                      0                                 1   \n",
              "2                                      0                                 0   \n",
              "3                                      1                                 0   \n",
              "4                                      0                                 0   \n",
              "\n",
              "   type_of_job_Informally employed  type_of_job_No Income  \\\n",
              "0                                0                      0   \n",
              "1                                0                      0   \n",
              "2                                0                      0   \n",
              "3                                0                      0   \n",
              "4                                1                      0   \n",
              "\n",
              "   type_of_job_Other Income  type_of_job_Remittance Dependent  \\\n",
              "0                         0                                 0   \n",
              "1                         0                                 0   \n",
              "2                         0                                 0   \n",
              "3                         0                                 0   \n",
              "4                         0                                 0   \n",
              "\n",
              "   type_of_job_Self employed  \n",
              "0                          1  \n",
              "1                          0  \n",
              "2                          1  \n",
              "3                          0  \n",
              "4                          0  \n",
              "\n",
              "[5 rows x 35 columns]"
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              "          document.querySelector('#df-fbfc1ed5-d22f-49be-aa4e-dac7e16cb97e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-fbfc1ed5-d22f-49be-aa4e-dac7e16cb97e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-2ooBypQ8CP",
        "outputId": "3f1b66df-355d-4e00-92a6-e4691146906a"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    0\n",
              "Name: has_a_bank_account, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2\n",
        "# Splitting dataset into Training set and Test set\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)"
      ],
      "metadata": {
        "id": "f92XvrSgQ-gf"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3\n",
        "# Normalization\n",
        "# PCA performs best with a normalized FEATURE SET (X). \n",
        "# We will perform standard scaler normalization to normalize our feature set\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "48sJjbKtTGHj",
        "outputId": "952b20aa-0ac1-4285-9247-d356506579b0"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-0416cd5a69be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    850\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    851\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 852\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    853\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    804\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    805\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 806\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    845\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m             \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"allow-nan\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 847\u001b[0;31m             \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_call\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    848\u001b[0m         )\n\u001b[1;32m    849\u001b[0m         \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 665\u001b[0;31m             \u001b[0mdtype_orig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mresult_type\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: The DType <class 'numpy.dtype[datetime64]'> could not be promoted by <class 'numpy.dtype[uint8]'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>, <class 'numpy.dtype[uint8]'>)"
          ]
        }
      ]
    }
  ]
}