hw1.ipynb

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      "source": [
        "#2: \n",
        "\n",
        "###Homework 1: \n",
        "\n",
        "####Kobie Williams \n",
        "\n",
        "#####Due Date: 2/21"
      ],
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      "source": [
        "#3: \n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4: \n",
        "df = pd.read_csv('https://drive.google.com/uc?export=download&id=1oQMcpoR3QC7w_sWIbtyid2N2j0o49_pk') \n"
      ],
      "metadata": {
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "#5: \n",
        "df.head(5)"
      ],
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      "execution_count": 3,
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              "        id  age   income  children  gender      region married  car  \\\n",
              "0  ID12101   48  17546.0         1  FEMALE  INNER_CITY      NO   NO   \n",
              "1  ID12102   40  30085.1         3    MALE        TOWN     YES  YES   \n",
              "2  ID12103   51  16575.4         0  FEMALE  INNER_CITY     YES  YES   \n",
              "3  ID12104   23  20375.4         3  FEMALE        TOWN     YES   NO   \n",
              "4  ID12105   57  50576.3         0  FEMALE       RURAL     YES   NO   \n",
              "\n",
              "  savings_acct current_acct mortgage  pep  \n",
              "0           NO           NO       NO  YES  \n",
              "1           NO          YES      YES   NO  \n",
              "2          YES          YES       NO   NO  \n",
              "3           NO          YES       NO   NO  \n",
              "4          YES           NO       NO   NO  "
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      "source": [
        "df = df.drop('id',axis=1)"
      ],
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "#5a: \n",
        "\n",
        "#Age \n",
        "#Min\n",
        "print(df['age'].min())\n",
        "  #Max\n",
        "print(df['age'].max())\n",
        "  #Average\n",
        "print(df['age'].mean())\n",
        "#Income \n",
        "  #Min\n",
        "print(df['income'].min())\n",
        "  #Max\n",
        "print(df['income'].max())\n",
        "  #Average\n",
        "print(df['income'].mean())\n",
        "#Children \n",
        "  #Min\n",
        "print(df['children'].min())\n",
        "  #Max\n",
        "print(df['children'].max())\n",
        "  #Average\n",
        "print(df['children'].mean())\n",
        "\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "U-pcNdDZ2rAb",
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      "execution_count": 5,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18\n",
            "67\n",
            "42.395\n",
            "5014.21\n",
            "63130.1\n",
            "27524.0312166667\n",
            "0\n",
            "3\n",
            "1.0116666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#5b: 296 customers own a car \n",
        "df['car'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3OvGwJi3EuH",
        "outputId": "7d22a005-b37d-480e-f8d2-8608ee03e7ec"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NO     304\n",
              "YES    296\n",
              "Name: car, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#5c: Rural has highest average number of children \n",
        "df.groupby('region').mean()"
      ],
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        "id": "qHwFJki-4A-A",
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            "text/plain": [
              "                  age        income  children\n",
              "region                                       \n",
              "INNER_CITY  41.992565  26843.999963  0.951673\n",
              "RURAL       43.010417  30027.605417  1.229167\n",
              "SUBURBAN    43.725806  28656.356935  0.967742\n",
              "TOWN        42.202312  26786.349653  1.000000"
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              "      <th>INNER_CITY</th>\n",
              "      <td>41.992565</td>\n",
              "      <td>26843.999963</td>\n",
              "      <td>0.951673</td>\n",
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              "      <th>RURAL</th>\n",
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    {
      "cell_type": "code",
      "source": [
        "#5d: Suburban has the highest standard deviation in age \n",
        "df.groupby('region').std()"
      ],
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      "execution_count": 8,
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            "text/plain": [
              "                  age        income  children\n",
              "region                                       \n",
              "INNER_CITY  14.987306  13015.863373  1.019167\n",
              "RURAL       14.396268  13699.154297  1.156029\n",
              "SUBURBAN    15.222039  13888.950040  1.039668\n",
              "TOWN        13.299309  11756.638715  1.056541"
            ],
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              "      <th>INNER_CITY</th>\n",
              "      <td>14.987306</td>\n",
              "      <td>13015.863373</td>\n",
              "      <td>1.019167</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#5e: The difference is ~7,417.44 dollars \n",
        "df.groupby('savings_acct').mean()"
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      "execution_count": 9,
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        {
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            "text/plain": [
              "                    age        income  children\n",
              "savings_acct                                   \n",
              "NO            38.430108  22405.999194  0.946237\n",
              "YES           44.176329  29823.436908  1.041063"
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              "      <td>22405.999194</td>\n",
              "      <td>0.946237</td>\n",
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              "    <tr>\n",
              "      <th>YES</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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      "cell_type": "code",
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        "df.describe(include='all') "
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      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age        income    children  gender      region married  car  \\\n",
              "count   600.000000    600.000000  600.000000     600         600     600  600   \n",
              "unique         NaN           NaN         NaN       2           4       2    2   \n",
              "top            NaN           NaN         NaN  FEMALE  INNER_CITY     YES   NO   \n",
              "freq           NaN           NaN         NaN     300         269     396  304   \n",
              "mean     42.395000  27524.031217    1.011667     NaN         NaN     NaN  NaN   \n",
              "std      14.424947  12899.468246    1.056752     NaN         NaN     NaN  NaN   \n",
              "min      18.000000   5014.210000    0.000000     NaN         NaN     NaN  NaN   \n",
              "25%      30.000000  17264.500000    0.000000     NaN         NaN     NaN  NaN   \n",
              "50%      42.000000  24925.300000    1.000000     NaN         NaN     NaN  NaN   \n",
              "75%      55.250000  36172.675000    2.000000     NaN         NaN     NaN  NaN   \n",
              "max      67.000000  63130.100000    3.000000     NaN         NaN     NaN  NaN   \n",
              "\n",
              "       savings_acct current_acct mortgage  pep  \n",
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              "unique            2            2        2    2  \n",
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              "freq            414          455      391  326  \n",
              "mean            NaN          NaN      NaN  NaN  \n",
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              "25%             NaN          NaN      NaN  NaN  \n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df.groupby('pep').apply(print)"
      ],
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        "id": "2D07kyfaPkk5",
        "outputId": "fbd6f34e-3bc5-41dc-8e19-102bd4f3f069"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     age    income  children  gender      region married  car savings_acct  \\\n",
            "1     40  30085.10         3    MALE        TOWN     YES  YES           NO   \n",
            "2     51  16575.40         0  FEMALE  INNER_CITY     YES  YES          YES   \n",
            "3     23  20375.40         3  FEMALE        TOWN     YES   NO           NO   \n",
            "4     57  50576.30         0  FEMALE       RURAL     YES   NO          YES   \n",
            "7     58  24946.60         0    MALE        TOWN     YES  YES          YES   \n",
            "..   ...       ...       ...     ...         ...     ...  ...          ...   \n",
            "592   48  20268.00         0    MALE        TOWN     YES   NO          YES   \n",
            "594   59  30971.80         3  FEMALE       RURAL     YES  YES          YES   \n",
            "595   61  47025.00         2  FEMALE  INNER_CITY      NO  YES          YES   \n",
            "596   30   9672.25         0  FEMALE  INNER_CITY     YES  YES          YES   \n",
            "598   29  14711.80         0    MALE  INNER_CITY     YES   NO          YES   \n",
            "\n",
            "    current_acct mortgage pep  \n",
            "1            YES      YES  NO  \n",
            "2            YES       NO  NO  \n",
            "3            YES       NO  NO  \n",
            "4             NO       NO  NO  \n",
            "7            YES       NO  NO  \n",
            "..           ...      ...  ..  \n",
            "592          YES       NO  NO  \n",
            "594          YES      YES  NO  \n",
            "595          YES      YES  NO  \n",
            "596          YES       NO  NO  \n",
            "598           NO      YES  NO  \n",
            "\n",
            "[326 rows x 11 columns]\n",
            "     age    income  children  gender      region married  car savings_acct  \\\n",
            "0     48  17546.00         1  FEMALE  INNER_CITY      NO   NO           NO   \n",
            "5     57  37869.60         2  FEMALE        TOWN     YES   NO          YES   \n",
            "6     22   8877.07         0    MALE       RURAL      NO   NO           NO   \n",
            "12    44  15735.80         1  FEMALE        TOWN     YES   NO          YES   \n",
            "13    66  55204.70         1  FEMALE        TOWN     YES  YES          YES   \n",
            "..   ...       ...       ...     ...         ...     ...  ...          ...   \n",
            "587   43  31273.80         1  FEMALE        TOWN      NO  YES          YES   \n",
            "591   40  31473.90         2  FEMALE  INNER_CITY      NO   NO          YES   \n",
            "593   65  51417.00         2    MALE    SUBURBAN     YES   NO          YES   \n",
            "597   31  15976.30         0  FEMALE        TOWN     YES  YES          YES   \n",
            "599   38  26671.60         0    MALE        TOWN      NO  YES           NO   \n",
            "\n",
            "    current_acct mortgage  pep  \n",
            "0             NO       NO  YES  \n",
            "5            YES       NO  YES  \n",
            "6            YES       NO  YES  \n",
            "12           YES      YES  YES  \n",
            "13           YES      YES  YES  \n",
            "..           ...      ...  ...  \n",
            "587           NO       NO  YES  \n",
            "591          YES      YES  YES  \n",
            "593          YES       NO  YES  \n",
            "597           NO       NO  YES  \n",
            "599          YES      YES  YES  \n",
            "\n",
            "[274 rows x 11 columns]\n"
          ]
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      "cell_type": "code",
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        "df.groupby('pep').mean()"
      ],
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          "height": 143
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        "id": "uBeWOzUlQXqG",
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      "execution_count": 12,
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          "data": {
            "text/plain": [
              "           age        income  children\n",
              "pep                                   \n",
              "NO   40.098160  24900.953374  1.067485\n",
              "YES  45.127737  30644.919453  0.945255"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5f: \n",
        "People who select \"yes\" to having a personal equity plan tend to be older, have more income, but they are less likely to have children. "
      ],
      "metadata": {
        "id": "7DTqrfXNRr9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a copy of the income column so we don't edit the values \n",
        "income_copy = df['income'].copy() \n",
        "\n",
        "#Creating the z-score values for each \n",
        "\n",
        "income_std = (income_copy - income_copy.mean()) / income_copy.std()\n",
        "print(income_std.mean()) \n",
        "print(income_std.std())\n",
        "income_std.head(5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXD8TvYs8EUy",
        "outputId": "c3b099b5-e42a-4331-a0e9-4b582acc8ca1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2.5135449277513545e-15\n",
            "1.0000000000000004\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.773523\n",
              "1    0.198541\n",
              "2   -0.848766\n",
              "3   -0.554180\n",
              "4    1.787071\n",
              "Name: income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6.  The mean of the normalized income attribute is about -2.514. The standard deviation of the normalized income attribute is about 1.000. This is because once we normalized the attribute we can tell truly how far each observation is from the mean of the original values by standard deviations. "
      ],
      "metadata": {
        "id": "WT_FwbQUXql6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Storing in age and not changing the values \n",
        "age_copy = df['age'].copy()\n",
        "\n",
        "age_bins = pd.qcut(age_copy, [0,.33,.67,1],  labels=['young','mid-age','old'])\n",
        "age_bins.value_counts()\n"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "OPWUkLFXOcGo",
        "outputId": "7f6a206b-ed0e-4b88-dd79-ade91722a811"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "young      209\n",
              "old        198\n",
              "mid-age    193\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. 209 young customers, 198 old customers, 193 mid-age customers "
      ],
      "metadata": {
        "id": "0mKF0kTwf5SA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#8: \n",
        "bank_norm = df.copy()\n",
        "\n",
        "bank_norm = pd.get_dummies(bank_norm)\n",
        "\n",
        "#min-max normalization \n",
        "bank_norm = (bank_norm - bank_norm.min()) / (bank_norm.max() - bank_norm.min())\n",
        "\n",
        "bank_norm.head(5)\n",
        "\n"
      ],
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      "outputs": [
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              "        age    income  children  gender_FEMALE  gender_MALE  \\\n",
              "0  0.612245  0.215634  0.333333            1.0          0.0   \n",
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              "3  0.102041  0.264320  1.000000            1.0          0.0   \n",
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              "   region_INNER_CITY  region_RURAL  region_SUBURBAN  region_TOWN  married_NO  \\\n",
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            ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(bank_norm['income'].mean()) \n",
        "print(bank_norm['income'].std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnZHNoq_QNun",
        "outputId": "315c5fa8-cbee-4ccd-8363-0df2a017d068"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3873264474942505\n",
            "0.22196112363814038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. The mean of income after min-max normalization is .38732645 and standard deviation is .22196114. These values are different than z-score normalization because we are keeping the values in the range of 0-1 as well as doing a different form of normalization. "
      ],
      "metadata": {
        "id": "2WjgHwbHgG_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#9: \n",
        "bank_standard = bank_norm.iloc[:, 0:]  \n",
        "bank_standard = pd.get_dummies(bank_norm) \n",
        "bank_standard.head(1)"
      ],
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          "height": 174
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        "id": "NPVj6MT9gmOo",
        "outputId": "f91e5778-79a2-4687-dcf2-6a4a846f1deb"
      },
      "execution_count": 17,
      "outputs": [
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              "        age    income  children  gender_FEMALE  gender_MALE  \\\n",
              "0  0.612245  0.215634  0.333333            1.0          0.0   \n",
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          },
          "metadata": {},
          "execution_count": 17
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#10a: \n",
        "corr = bank_standard.corr() \n",
        "corr"
      ],
      "metadata": {
        "id": "uDADcjZkcoNB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "outputId": "4c3ad228-f196-4677-9923-06580b30195f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                        age    income  children  gender_FEMALE  gender_MALE  \\\n",
              "age                1.000000  0.752726  0.023572       0.090081    -0.090081   \n",
              "income             0.752726  1.000000  0.036761       0.023845    -0.023845   \n",
              "children           0.023572  0.036761  1.000000       0.014206    -0.014206   \n",
              "gender_FEMALE      0.090081  0.023845  0.014206       1.000000    -1.000000   \n",
              "gender_MALE       -0.090081 -0.023845 -0.014206      -1.000000     1.000000   \n",
              "region_INNER_CITY -0.025171 -0.047564 -0.051222      -0.023459     0.023459   \n",
              "region_RURAL       0.018635  0.084776  0.089902      -0.009092     0.009092   \n",
              "region_SUBURBAN    0.031345  0.029824 -0.014122      -0.010951     0.010951   \n",
              "region_TOWN       -0.008510 -0.036431 -0.007033       0.040472    -0.040472   \n",
              "married_NO        -0.010394  0.008386  0.048716       0.021110    -0.021110   \n",
              "married_YES        0.010394 -0.008386 -0.048716      -0.021110     0.021110   \n",
              "car_NO            -0.077733 -0.081556 -0.036455       0.006667    -0.006667   \n",
              "car_YES            0.077733  0.081556  0.036455      -0.006667     0.006667   \n",
              "savings_acct_NO   -0.184389 -0.266164 -0.041536       0.007207    -0.007207   \n",
              "savings_acct_YES   0.184389  0.266164  0.041536      -0.007207     0.007207   \n",
              "current_acct_NO    0.035312 -0.031616 -0.006238      -0.019466     0.019466   \n",
              "current_acct_YES  -0.035312  0.031616  0.006238       0.019466    -0.019466   \n",
              "mortgage_NO        0.016154  0.014662  0.074339       0.066465    -0.066465   \n",
              "mortgage_YES      -0.016154 -0.014662 -0.074339      -0.066465     0.066465   \n",
              "pep_NO            -0.173825 -0.221991  0.057663       0.046843    -0.046843   \n",
              "pep_YES            0.173825  0.221991 -0.057663      -0.046843     0.046843   \n",
              "\n",
              "                   region_INNER_CITY  region_RURAL  region_SUBURBAN  \\\n",
              "age                        -0.025171      0.018635         0.031345   \n",
              "income                     -0.047564      0.084776         0.029824   \n",
              "children                   -0.051222      0.089902        -0.014122   \n",
              "gender_FEMALE              -0.023459     -0.009092        -0.010951   \n",
              "gender_MALE                 0.023459      0.009092         0.010951   \n",
              "region_INNER_CITY           1.000000     -0.393444        -0.306032   \n",
              "region_RURAL               -0.393444      1.000000        -0.148158   \n",
              "region_SUBURBAN            -0.306032     -0.148158         1.000000   \n",
              "region_TOWN                -0.573814     -0.277798        -0.216080   \n",
              "married_NO                 -0.003254      0.022649        -0.012483   \n",
              "married_YES                 0.003254     -0.022649         0.012483   \n",
              "car_NO                      0.018143     -0.024006         0.061184   \n",
              "car_YES                    -0.018143      0.024006        -0.061184   \n",
              "savings_acct_NO             0.091373     -0.036960        -0.002605   \n",
              "savings_acct_YES           -0.091373      0.036960         0.002605   \n",
              "current_acct_NO            -0.007894      0.008496        -0.038157   \n",
              "current_acct_YES            0.007894     -0.008496         0.038157   \n",
              "mortgage_NO                -0.002098      0.051908        -0.004635   \n",
              "mortgage_YES                0.002098     -0.051908         0.004635   \n",
              "pep_NO                     -0.001054     -0.019714        -0.062508   \n",
              "pep_YES                     0.001054      0.019714         0.062508   \n",
              "\n",
              "                   region_TOWN  married_NO  ...    car_NO   car_YES  \\\n",
              "age                  -0.008510   -0.010394  ... -0.077733  0.077733   \n",
              "income               -0.036431    0.008386  ... -0.081556  0.081556   \n",
              "children             -0.007033    0.048716  ... -0.036455  0.036455   \n",
              "gender_FEMALE         0.040472    0.021110  ...  0.006667 -0.006667   \n",
              "gender_MALE          -0.040472   -0.021110  ... -0.006667  0.006667   \n",
              "region_INNER_CITY    -0.573814   -0.003254  ...  0.018143 -0.018143   \n",
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              "region_SUBURBAN      -0.216080   -0.012483  ...  0.061184 -0.061184   \n",
              "region_TOWN           1.000000   -0.006369  ... -0.041604  0.041604   \n",
              "married_NO           -0.006369    1.000000  ... -0.009571  0.009571   \n",
              "married_YES           0.006369   -1.000000  ...  0.009571 -0.009571   \n",
              "car_NO               -0.041604   -0.009571  ...  1.000000 -1.000000   \n",
              "car_YES               0.041604    0.009571  ... -1.000000  1.000000   \n",
              "savings_acct_NO      -0.068654    0.028604  ...  0.034310 -0.034310   \n",
              "savings_acct_YES      0.068654   -0.028604  ... -0.034310  0.034310   \n",
              "current_acct_NO       0.027431   -0.059996  ... -0.034783  0.034783   \n",
              "current_acct_YES     -0.027431    0.059996  ...  0.034783 -0.034783   \n",
              "mortgage_NO          -0.036591   -0.021711  ... -0.007743  0.007743   \n",
              "mortgage_YES          0.036591    0.021711  ...  0.007743 -0.007743   \n",
              "pep_NO                0.059115   -0.189578  ...  0.018917 -0.018917   \n",
              "pep_YES              -0.059115    0.189578  ... -0.018917  0.018917   \n",
              "\n",
              "                   savings_acct_NO  savings_acct_YES  current_acct_NO  \\\n",
              "age                      -0.184389          0.184389         0.035312   \n",
              "income                   -0.266164          0.266164        -0.031616   \n",
              "children                 -0.041536          0.041536        -0.006238   \n",
              "gender_FEMALE             0.007207         -0.007207        -0.019466   \n",
              "gender_MALE              -0.007207          0.007207         0.019466   \n",
              "region_INNER_CITY         0.091373         -0.091373        -0.007894   \n",
              "region_RURAL             -0.036960          0.036960         0.008496   \n",
              "region_SUBURBAN          -0.002605          0.002605        -0.038157   \n",
              "region_TOWN              -0.068654          0.068654         0.027431   \n",
              "married_NO                0.028604         -0.028604        -0.059996   \n",
              "married_YES              -0.028604          0.028604         0.059996   \n",
              "car_NO                    0.034310         -0.034310        -0.034783   \n",
              "car_YES                  -0.034310          0.034310         0.034783   \n",
              "savings_acct_NO           1.000000         -1.000000         0.042511   \n",
              "savings_acct_YES         -1.000000          1.000000        -0.042511   \n",
              "current_acct_NO           0.042511         -0.042511         1.000000   \n",
              "current_acct_YES         -0.042511          0.042511        -1.000000   \n",
              "mortgage_NO              -0.001588          0.001588        -0.036704   \n",
              "mortgage_YES              0.001588         -0.001588         0.036704   \n",
              "pep_NO                   -0.072779          0.072779         0.025141   \n",
              "pep_YES                   0.072779         -0.072779        -0.025141   \n",
              "\n",
              "                   current_acct_YES  mortgage_NO  mortgage_YES    pep_NO  \\\n",
              "age                       -0.035312     0.016154     -0.016154 -0.173825   \n",
              "income                     0.031616     0.014662     -0.014662 -0.221991   \n",
              "children                   0.006238     0.074339     -0.074339  0.057663   \n",
              "gender_FEMALE              0.019466     0.066465     -0.066465  0.046843   \n",
              "gender_MALE               -0.019466    -0.066465      0.066465 -0.046843   \n",
              "region_INNER_CITY          0.007894    -0.002098      0.002098 -0.001054   \n",
              "region_RURAL              -0.008496     0.051908     -0.051908 -0.019714   \n",
              "region_SUBURBAN            0.038157    -0.004635      0.004635 -0.062508   \n",
              "region_TOWN               -0.027431    -0.036591      0.036591  0.059115   \n",
              "married_NO                 0.059996    -0.021711      0.021711 -0.189578   \n",
              "married_YES               -0.059996     0.021711     -0.021711  0.189578   \n",
              "car_NO                     0.034783    -0.007743      0.007743  0.018917   \n",
              "car_YES                   -0.034783     0.007743     -0.007743 -0.018917   \n",
              "savings_acct_NO           -0.042511    -0.001588      0.001588 -0.072779   \n",
              "savings_acct_YES           0.042511     0.001588     -0.001588  0.072779   \n",
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              "mortgage_YES              -0.036704    -1.000000      1.000000  0.024182   \n",
              "pep_NO                    -0.025141    -0.024182      0.024182  1.000000   \n",
              "pep_YES                    0.025141     0.024182     -0.024182 -1.000000   \n",
              "\n",
              "                    pep_YES  \n",
              "age                0.173825  \n",
              "income             0.221991  \n",
              "children          -0.057663  \n",
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              "      <td>-0.090081</td>\n",
              "      <td>-0.023845</td>\n",
              "      <td>-0.014206</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.023459</td>\n",
              "      <td>0.009092</td>\n",
              "      <td>0.010951</td>\n",
              "      <td>-0.040472</td>\n",
              "      <td>-0.021110</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.006667</td>\n",
              "      <td>0.006667</td>\n",
              "      <td>-0.007207</td>\n",
              "      <td>0.007207</td>\n",
              "      <td>0.019466</td>\n",
              "      <td>-0.019466</td>\n",
              "      <td>-0.066465</td>\n",
              "      <td>0.066465</td>\n",
              "      <td>-0.046843</td>\n",
              "      <td>0.046843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region_INNER_CITY</th>\n",
              "      <td>-0.025171</td>\n",
              "      <td>-0.047564</td>\n",
              "      <td>-0.051222</td>\n",
              "      <td>-0.023459</td>\n",
              "      <td>0.023459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.393444</td>\n",
              "      <td>-0.306032</td>\n",
              "      <td>-0.573814</td>\n",
              "      <td>-0.003254</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018143</td>\n",
              "      <td>-0.018143</td>\n",
              "      <td>0.091373</td>\n",
              "      <td>-0.091373</td>\n",
              "      <td>-0.007894</td>\n",
              "      <td>0.007894</td>\n",
              "      <td>-0.002098</td>\n",
              "      <td>0.002098</td>\n",
              "      <td>-0.001054</td>\n",
              "      <td>0.001054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region_RURAL</th>\n",
              "      <td>0.018635</td>\n",
              "      <td>0.084776</td>\n",
              "      <td>0.089902</td>\n",
              "      <td>-0.009092</td>\n",
              "      <td>0.009092</td>\n",
              "      <td>-0.393444</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.148158</td>\n",
              "      <td>-0.277798</td>\n",
              "      <td>0.022649</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.024006</td>\n",
              "      <td>0.024006</td>\n",
              "      <td>-0.036960</td>\n",
              "      <td>0.036960</td>\n",
              "      <td>0.008496</td>\n",
              "      <td>-0.008496</td>\n",
              "      <td>0.051908</td>\n",
              "      <td>-0.051908</td>\n",
              "      <td>-0.019714</td>\n",
              "      <td>0.019714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region_SUBURBAN</th>\n",
              "      <td>0.031345</td>\n",
              "      <td>0.029824</td>\n",
              "      <td>-0.014122</td>\n",
              "      <td>-0.010951</td>\n",
              "      <td>0.010951</td>\n",
              "      <td>-0.306032</td>\n",
              "      <td>-0.148158</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.216080</td>\n",
              "      <td>-0.012483</td>\n",
              "      <td>...</td>\n",
              "      <td>0.061184</td>\n",
              "      <td>-0.061184</td>\n",
              "      <td>-0.002605</td>\n",
              "      <td>0.002605</td>\n",
              "      <td>-0.038157</td>\n",
              "      <td>0.038157</td>\n",
              "      <td>-0.004635</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>-0.062508</td>\n",
              "      <td>0.062508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region_TOWN</th>\n",
              "      <td>-0.008510</td>\n",
              "      <td>-0.036431</td>\n",
              "      <td>-0.007033</td>\n",
              "      <td>0.040472</td>\n",
              "      <td>-0.040472</td>\n",
              "      <td>-0.573814</td>\n",
              "      <td>-0.277798</td>\n",
              "      <td>-0.216080</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.006369</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.041604</td>\n",
              "      <td>0.041604</td>\n",
              "      <td>-0.068654</td>\n",
              "      <td>0.068654</td>\n",
              "      <td>0.027431</td>\n",
              "      <td>-0.027431</td>\n",
              "      <td>-0.036591</td>\n",
              "      <td>0.036591</td>\n",
              "      <td>0.059115</td>\n",
              "      <td>-0.059115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>married_NO</th>\n",
              "      <td>-0.010394</td>\n",
              "      <td>0.008386</td>\n",
              "      <td>0.048716</td>\n",
              "      <td>0.021110</td>\n",
              "      <td>-0.021110</td>\n",
              "      <td>-0.003254</td>\n",
              "      <td>0.022649</td>\n",
              "      <td>-0.012483</td>\n",
              "      <td>-0.006369</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009571</td>\n",
              "      <td>0.009571</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>-0.028604</td>\n",
              "      <td>-0.059996</td>\n",
              "      <td>0.059996</td>\n",
              "      <td>-0.021711</td>\n",
              "      <td>0.021711</td>\n",
              "      <td>-0.189578</td>\n",
              "      <td>0.189578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>married_YES</th>\n",
              "      <td>0.010394</td>\n",
              "      <td>-0.008386</td>\n",
              "      <td>-0.048716</td>\n",
              "      <td>-0.021110</td>\n",
              "      <td>0.021110</td>\n",
              "      <td>0.003254</td>\n",
              "      <td>-0.022649</td>\n",
              "      <td>0.012483</td>\n",
              "      <td>0.006369</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.009571</td>\n",
              "      <td>-0.009571</td>\n",
              "      <td>-0.028604</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>0.059996</td>\n",
              "      <td>-0.059996</td>\n",
              "      <td>0.021711</td>\n",
              "      <td>-0.021711</td>\n",
              "      <td>0.189578</td>\n",
              "      <td>-0.189578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_NO</th>\n",
              "      <td>-0.077733</td>\n",
              "      <td>-0.081556</td>\n",
              "      <td>-0.036455</td>\n",
              "      <td>0.006667</td>\n",
              "      <td>-0.006667</td>\n",
              "      <td>0.018143</td>\n",
              "      <td>-0.024006</td>\n",
              "      <td>0.061184</td>\n",
              "      <td>-0.041604</td>\n",
              "      <td>-0.009571</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.034310</td>\n",
              "      <td>-0.034310</td>\n",
              "      <td>-0.034783</td>\n",
              "      <td>0.034783</td>\n",
              "      <td>-0.007743</td>\n",
              "      <td>0.007743</td>\n",
              "      <td>0.018917</td>\n",
              "      <td>-0.018917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_YES</th>\n",
              "      <td>0.077733</td>\n",
              "      <td>0.081556</td>\n",
              "      <td>0.036455</td>\n",
              "      <td>-0.006667</td>\n",
              "      <td>0.006667</td>\n",
              "      <td>-0.018143</td>\n",
              "      <td>0.024006</td>\n",
              "      <td>-0.061184</td>\n",
              "      <td>0.041604</td>\n",
              "      <td>0.009571</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.034310</td>\n",
              "      <td>0.034310</td>\n",
              "      <td>0.034783</td>\n",
              "      <td>-0.034783</td>\n",
              "      <td>0.007743</td>\n",
              "      <td>-0.007743</td>\n",
              "      <td>-0.018917</td>\n",
              "      <td>0.018917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>savings_acct_NO</th>\n",
              "      <td>-0.184389</td>\n",
              "      <td>-0.266164</td>\n",
              "      <td>-0.041536</td>\n",
              "      <td>0.007207</td>\n",
              "      <td>-0.007207</td>\n",
              "      <td>0.091373</td>\n",
              "      <td>-0.036960</td>\n",
              "      <td>-0.002605</td>\n",
              "      <td>-0.068654</td>\n",
              "      <td>0.028604</td>\n",
              "      <td>...</td>\n",
              "      <td>0.034310</td>\n",
              "      <td>-0.034310</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.042511</td>\n",
              "      <td>-0.042511</td>\n",
              "      <td>-0.001588</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>-0.072779</td>\n",
              "      <td>0.072779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>savings_acct_YES</th>\n",
              "      <td>0.184389</td>\n",
              "      <td>0.266164</td>\n",
              "      <td>0.041536</td>\n",
              "      <td>-0.007207</td>\n",
              "      <td>0.007207</td>\n",
              "      <td>-0.091373</td>\n",
              "      <td>0.036960</td>\n",
              "      <td>0.002605</td>\n",
              "      <td>0.068654</td>\n",
              "      <td>-0.028604</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.034310</td>\n",
              "      <td>0.034310</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.042511</td>\n",
              "      <td>0.042511</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>-0.001588</td>\n",
              "      <td>0.072779</td>\n",
              "      <td>-0.072779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>current_acct_NO</th>\n",
              "      <td>0.035312</td>\n",
              "      <td>-0.031616</td>\n",
              "      <td>-0.006238</td>\n",
              "      <td>-0.019466</td>\n",
              "      <td>0.019466</td>\n",
              "      <td>-0.007894</td>\n",
              "      <td>0.008496</td>\n",
              "      <td>-0.038157</td>\n",
              "      <td>0.027431</td>\n",
              "      <td>-0.059996</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.034783</td>\n",
              "      <td>0.034783</td>\n",
              "      <td>0.042511</td>\n",
              "      <td>-0.042511</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.036704</td>\n",
              "      <td>0.036704</td>\n",
              "      <td>0.025141</td>\n",
              "      <td>-0.025141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>current_acct_YES</th>\n",
              "      <td>-0.035312</td>\n",
              "      <td>0.031616</td>\n",
              "      <td>0.006238</td>\n",
              "      <td>0.019466</td>\n",
              "      <td>-0.019466</td>\n",
              "      <td>0.007894</td>\n",
              "      <td>-0.008496</td>\n",
              "      <td>0.038157</td>\n",
              "      <td>-0.027431</td>\n",
              "      <td>0.059996</td>\n",
              "      <td>...</td>\n",
              "      <td>0.034783</td>\n",
              "      <td>-0.034783</td>\n",
              "      <td>-0.042511</td>\n",
              "      <td>0.042511</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.036704</td>\n",
              "      <td>-0.036704</td>\n",
              "      <td>-0.025141</td>\n",
              "      <td>0.025141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mortgage_NO</th>\n",
              "      <td>0.016154</td>\n",
              "      <td>0.014662</td>\n",
              "      <td>0.074339</td>\n",
              "      <td>0.066465</td>\n",
              "      <td>-0.066465</td>\n",
              "      <td>-0.002098</td>\n",
              "      <td>0.051908</td>\n",
              "      <td>-0.004635</td>\n",
              "      <td>-0.036591</td>\n",
              "      <td>-0.021711</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007743</td>\n",
              "      <td>0.007743</td>\n",
              "      <td>-0.001588</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>-0.036704</td>\n",
              "      <td>0.036704</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.024182</td>\n",
              "      <td>0.024182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mortgage_YES</th>\n",
              "      <td>-0.016154</td>\n",
              "      <td>-0.014662</td>\n",
              "      <td>-0.074339</td>\n",
              "      <td>-0.066465</td>\n",
              "      <td>0.066465</td>\n",
              "      <td>0.002098</td>\n",
              "      <td>-0.051908</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>0.036591</td>\n",
              "      <td>0.021711</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007743</td>\n",
              "      <td>-0.007743</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>-0.001588</td>\n",
              "      <td>0.036704</td>\n",
              "      <td>-0.036704</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.024182</td>\n",
              "      <td>-0.024182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pep_NO</th>\n",
              "      <td>-0.173825</td>\n",
              "      <td>-0.221991</td>\n",
              "      <td>0.057663</td>\n",
              "      <td>0.046843</td>\n",
              "      <td>-0.046843</td>\n",
              "      <td>-0.001054</td>\n",
              "      <td>-0.019714</td>\n",
              "      <td>-0.062508</td>\n",
              "      <td>0.059115</td>\n",
              "      <td>-0.189578</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018917</td>\n",
              "      <td>-0.018917</td>\n",
              "      <td>-0.072779</td>\n",
              "      <td>0.072779</td>\n",
              "      <td>0.025141</td>\n",
              "      <td>-0.025141</td>\n",
              "      <td>-0.024182</td>\n",
              "      <td>0.024182</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pep_YES</th>\n",
              "      <td>0.173825</td>\n",
              "      <td>0.221991</td>\n",
              "      <td>-0.057663</td>\n",
              "      <td>-0.046843</td>\n",
              "      <td>0.046843</td>\n",
              "      <td>0.001054</td>\n",
              "      <td>0.019714</td>\n",
              "      <td>0.062508</td>\n",
              "      <td>-0.059115</td>\n",
              "      <td>0.189578</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018917</td>\n",
              "      <td>0.018917</td>\n",
              "      <td>0.072779</td>\n",
              "      <td>-0.072779</td>\n",
              "      <td>-0.025141</td>\n",
              "      <td>0.025141</td>\n",
              "      <td>0.024182</td>\n",
              "      <td>-0.024182</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f5a9dec-cd2d-4b61-b070-0afa1d8e9e8c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f5a9dec-cd2d-4b61-b070-0afa1d8e9e8c 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-9f5a9dec-cd2d-4b61-b070-0afa1d8e9e8c');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#10b: \n",
        "sns.heatmap(corr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "8Jug4mO5g04u",
        "outputId": "90b23f72-aaf6-4764-ec9f-7b715ef57705"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f450bf295d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#11\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "ax = plt.subplot()\n",
        "im = ax.imshow(corr)\n",
        "\n",
        "# create an axes on the right side of ax. The width of cax will be 5%\n",
        "# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n",
        "divider = make_axes_locatable(ax)\n",
        "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "#we want labels of all of the attributes and we want that to be\n",
        "#... the correlation matrix \n",
        "\n",
        "plt.colorbar(im, cax=cax)\n",
        "plt.matshow(corr)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "U_Ypgvu7jEh5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "04dd5bef-d7bf-40bf-af3a-3f98aed973f8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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9og/bNLM+60c/uYgYl3Q+sBoYBlZGxDpJFwNrI2IV8AXgcOCfysT0/69sSX0dcJWkOkWsuCQiDm6QkzQbeC/w0YZl58LeBNNnAh+TNA48Cywtk02b2QAJoJ5Mlt6xruKq7eamZZ9umD+lzfv+HXhjX3aiQU9BLiJ2Akc2LbuyYf4y4LJetmFmUyCAio548LAuMwM8dtXMqs5BzsyqKz82dbpxkDOzgs/kBk+9i9EJMZIs20VugBjO1fnwktzHHMP5Xu3ZnAzZkQwPLr2yc6HSaa9YnKvzi7+arjObk2F8du4zGt6RH20xlBwcMfaSXMHhXV10P022aM7enD+e3W/a1bFMzGz6HAOiT62rg2ZaBzkz6ycHOTOrMl+umlmlOciZWWW5M7CZVZ07A5tZtbl11cyqrJuHfU4nDnJm1s3z4qYdBzkzA+SGBzOrOJ/JTSFBJEaxKJ+rJJ2EJJIJbwDqI7lySo7WGsrluwEgst9c8nCyQ7UAVj96V6rcq2/oYlhX8rvUWO57rM/KD5GrJb/zkadzQ6vGD83/DmW/88xQrQmH3H1Y5+0+2+JzzH9k08pgBjkzm1oV7ieX+m9R0kpJWyTd27BsnqQ1ktaXP+e2ee+yssx6Scv6teNm1l+KzlOqHmmJpAckbZB0QYv1syRdX66/Q9JxDes+VS5/QNJp/Tiu7OMSrgaWNC27ALglIhYCt5SvX0DSPOAi4CSKzNoXtQuGZnaQ9SFbl6Rh4HKKLH6LgLMlLWoqdg7wZES8BrgU+Fz53kUU2b1eTxFv/rasryepIBcRtwHbmhafAVxTzl8DtMp2fRqwJiK2RcSTwBr2DZZmVh0nAhsiYmNEPAdcRxErGjXGjhuB96hI23UGcF1E7ImInwMbyvp60kve1aMi4rFy/hfAUS3KHAM80vB6U7lsH5KWS1oraW1tx84edsvMJiN5uTp/4u+0nJY3VZP5m99bJiLGge0UCbHS8aIbfWl4KPOq9tQAHRErgBUAs165oKKN2WYDKsgO69oaEaMHeG/6qpczucclHQ1Q/tzSosxmYEHD62PLZWY2aPpwT47c3/zeMpJmAC+lSDp/QOJFL0FuFTDRWroM+FaLMquBUyXNLRscTi2XmdmA6VPr6p3AQknHS5pJ0ZCwqqlMY+w4E/hemXR+FbC0bH09HlgI/KjX40pdrkq6FjiZ4np8E0WL6SXADZLOAR4GzirLjgLnRsRHImKbpM9SHDjAxRHR3IBhZoOgDzeJImJc0vkUJzPDwMqIWCfpYmBtRKwCvgJ8TdIGigbNpeV710m6AbgPGAfOi4guuvy3phjAh0gdcuyCOPb8j3cs181dwOzohHo24Q2kfykyozcAhp7Ld8aszc599zOeyW28m5Ee2eN58Kz+J8dZ/zcnpcoNt+rR36PaobkhARrrolNtsmh0cTiZURSbL/0Sex55ZO/WZy1YEMf+Yee/uY1/8sc/nm735Dziwcy66uw73TjImVnBD800syrzmZyZVZuDnJlVlu/JmVnlOciZWZVlH+463fS/M5GZ2QDxmZyZFXy5amaV5YaHqRXDwdi8noesvUB2qM2BGJKTTVbSTfKVdFKVw5NDkcbzx51NOnMgkuMs/Ie3pcplh2B1IztU7EAMDewmyVFqCGOrr9tBzswqzUHOzKpKVLd11UHOzHxPzsxeBBzkzKzSHOTMrMqqernasU1c0kpJWyTd27DsC5L+Q9Ldkm6SNKfNex+SdI+kuySt7eeOm1mf9SeRzcDJdPy5mn0TQq8B3hARbwJ+BnxqP+9/V0Qsnm6PTDZ7UYmidbXT1CtJ8yStkbS+/Dm3RZnFkn4oaV15IvVbDeuulvTz8sTpLkkdO2N2DHIRcRtFsonGZd8pk8IC3E6ROszMprOpOZO7ALglIhYCt5Svm+0Cfj8iXk9xgvWlpqvFPylPnBZHRMde5P24J/dfgevbrAvgO2Xi6avKBNItlZm4lwPMeMlcDnu4866N7Mzv5M5jct9QfWb/e6vXjkgmndme/zpqyf2c8Uyul/747Px/0xrL1ZlNOgP5kQzrf/eKVLluRlusvyy5n9lfjW6CQXbUTDdJjg5P/L61uAE3RffkzqDI/AdwDfB94E8bC0TEzxrmH5W0BXgZ8NRkNtjTU0gkXUiROuzrbYq8IyLeCrwPOE/SO9vVFRErImI0IkZnHDa7l90ys8nIncnNl7S2YVre5VaOiojHyvlfAEftr7CkE4GZwIMNi/+yvIy9VNKsThuc9JmcpA8D7wfeE23yGkbE5vLnFkk3AScCt012m2Z2gOQvR7d2ur8u6bvAy1usuvAFm4yI8iqvXT1HA18DlkXExKXGpyiC40xgBcVZ4MX7259JBTlJS4D/CfxaROxqU2Y2MBQRz5Tzp3baGTM7OET/Llcj4pS225Eel3R0RDxWBrEtbcq9BPg2cGFE3N5Q98RZ4B5Jfw98stP+ZLqQXAv8EHitpE2SzgEuA44A1pQtHFeWZV8h6ebyrUcBP5D0U+BHwLcj4l87bc/MDo6J3Kv7m/pgFbCsnF8GfGuf/ZBmAjcBX42IG5vWHV3+FPBB4N7m9zfreCYXEWe3WPyVNmUfBU4v5zcCb+5Uv5kNiKlpeLgEuKE8WXoYOAtA0ihwbkR8pFz2TuDI8rYYwIfLltSvS3oZxcnnXcC5nTboEQ9mVpiCIBcRTwDvabF8LfCRcv4fgH9o8/53d7tNBzkz81NIzOxFwEHOzKrMD82cQvVD6+x5w7Mdyw0dtiddZ23zEaly6qJneba3+sxf5j7msZd2MeogmZNhbE5utMXwjlzOCMjnosjmRIB8TobsSIZszgiAhV9L5o1IjgrpKk9I8uypm5wnI9s6f5eq7buPvlw1s+qaxk8Z6cRBzswKDnJmVlX9HPEwaBzkzAwA1asZ5RzkzMz35Mys+ny5ambV5iBnZlXmMzkzqzYHOTOrrPCwrqk1PkR928yOxXZvPiRdpWbk/pvq5ouuj+TK1Q5NbrvFUJt2hpJDh2I4V24oP2qIWvKzPBCySWeyQ7UA1v9ef5PjbPx8ftvZ4WzdDDfM/F5GU3XuJ2dm1dc6Vcu0l3n8+UpJWyTd27DsM5I2NyR4Pb3Ne5dIekDSBkmt8iua2YCYosefT7nMYyKupkjw2uzShgSvNzevlDQMXE6RjnARcLakRb3srJkdIJl0hFUNchFxG7BtEnWfCGyIiI0R8RxwHUViWTMbQKp3nnrehjRP0hpJ68ufc9uUqzVcKa5qWH68pDvKq8Pry6Q3+9VLcunzywSvK9vs6DHAIw2vN5XLWpK0fCJhbW3Hjh52y8wmYyqCHHABcEtELARuKV+38mzDleIHGpZ/juIq8jXAk8A5nTY42SB3BfBqYDHwGPDFSdazV0SsiIjRiBgdPvzwXqszs24ERcNDp6l3ZwDXlPPXUKQVTCnTEL4bmEhTmHr/pIJcRDweEbUyq/XfUVyaNtsMLGh4fWy5zMwGULLhYf7EFVc5Le9yM0c1JIj+BUV+5lYOKeu/XdJEIDsSeCoixsvX+706nDCpLiQTGbDLl79O6wSvdwILJR1PEdyWAr89me2Z2RTInahtjYjR/RWQ9F3g5S1WXfiCzUWE1LbN9lURsVnSCcD3JN0DbE/tYZOOQU7StcDJFBF8E3ARcLKkxRQfy0PAR8uyrwC+HBGnR8S4pPOB1cAwsDIi1k1mJ83swOpnZ+CIOKXtdqTHJ06SJB0NbGlTx+by50ZJ3wfeAnwDmCNpRnk2l7o67BjkIuLsFou/0qbso8DpDa9vBvbpXtJR5JKgZHuLQz7xS3YUA8DQWK5cJM+X1c2og+SxD+3O3ZEYe0kXiVKeziW9GU8mfoEukt4k/xCzSWeg/8lxTvhGblQG5I+7eYTC/qQSDTXXFzFVD81cBSwDLil/fqu5QNmQuSsi9kiaD7wd+Hx55ncrcCZFb42W72/WS+uqmVXJ1PSTuwR4r6T1wCnlaySNSvpyWeZ1wFpJPwVuBS6JiPvKdX8KfELSBop7dC1PuBp5WJeZAVMzoiEingDe02L5WuAj5fy/A29s8/6NtG7obMtBzsyKszTneDCzSqtmjHOQM7PCdB2A34mDnJkBTkloZlU2jZ8y0omDnJmVnYGrGeUc5Mys4BwPU0hQm9X5f5VuciLUs6MjuvjPbGhXrud/bShXaf2Q/Ma1J3fs2ZvJw7vy/cLHszkrknkoAOojyR1NFutm29mcDNmRDBs/dFV629nRFg9+IZ83YrJ8Jmdm1eV7cmZWbVM2dnXKOciZWcGXq2ZWWU4ubWaV5zM5M6u0asY4BzkzK6hezevVzOPPVwLvB7ZExBvKZdcDry2LzKFILrFPhx9JDwHPADVgvNOz4c3sIAle1J2BrwYuA746sSAifmtiXtIX2X+CiXdFxNbJ7qCZHXgiXrydgSPiNknHtVpX5kE8iyIXoplNZy/WINfBfwEej4j1bdYH8J0y7dhVEbGiXUVl/sblAMNz5+aSxHSR3GNkW24IVjd1js3JJX/JJpMZfiY/tKqeGPYGXST7qecPPJ3AJ/mRF4WT5bK72cXfa/YzyiadyQ7Vgi6S49yYT44ztCexn60+nykIcpLmAdcDx1Fk+jsrIp5sKvMu4NKGRb8CLI2Ib0q6Gvg1nr96/HBE7PdD7DWRzdnAtftZ/46IeCvwPuA8Se9sVzAiVkTEaESMDs+e3eNumVlXJu7JdZp6dwFwS0QsBG4pX79wVyJujYjF5X3+dwO7gO80FPmTifWdAhz0EOQkzQB+gyIqt9SQO3ELcBNdJqAws6mjer3j1AdnANeU89cAH+xQ/kzgXyJi12Q32MuZ3CnAf0TEplYrJc2WdMTEPHAqcG8P2zOzAyaKy9VOU++OiojHyvlfAEd1KL+Ufa8W/1LS3ZIulTSr0wY7BjlJ1wI/BF4raZOkc9ptXNIrJE0kkz4K+EGZO/FHwLcj4l87bc/MDoIgG+TmS1rbMC1vrkrSdyXd22I64wWbjNjvs08kHU2RmnB1w+JPUdyj+8/APIo8rPuVaV09u83yD7dY9ihwejm/EXhzp/rNbEDkrka3durvGhGntFsn6XFJR0fEY2UQ27Kfqs4CboqIvU1dDWeBeyT9PfDJTjvca8ODmVWEIjpOfbAKWFbOLwO+tZ+y+zRsloFxovvaB0ncAnOQM7PC1NyTuwR4r6T1FPf1LwGQNCrpyxOFyr65C4D/0/T+r0u6B7gHmA/8RacNeuyqmRUBrHbgx3VFxBPAe1osXwt8pOH1Q8AxLcp1PfDAQc7MCh7xMHWGxmHWE52vpLM97wF2nDCeKqcuev7PeDrXpT+bpCVm5H/Jhnfn9nPspbn/nWdvzg9P2P2mXJel+OUh6Tqz3+XQc8njnpcbjQKgZJ2R/NXoJulMdiTDxjP7mxxna+zcd6GDnJlVVgDO8WBm1RUQ1XzWkoOcmRVnclPQ8HAwOMiZWcH35Mys0hzkzKy6+tbZd+A4yJlZ2brqe3JmVmU+kzOz6pqaYV0Hg4OcmZW35Bzkpkz90Dq7X/9sx3IzRvJDd0Z+nssbkR26A1Cbnful0Fiu0vpIN9vOHfvI9txwrexQLYBD7j4sV+f8/B9N9thrhyePO5u4qItt12f1Pwikks7Q/+Q4J57W4vuu6IiHzJOBF0i6VdJ9ktZJ+sNy+TxJayStL3/ObfP+ZWWZ9ZKWtSpjZgNgah61NOUy/42MA38cEYuAX6XIurWIRNadMv3YRcBJFElsLmoXDM3sIIooWlc7TdNQxyAXEY9FxE/K+WeA+yme85TJunMasCYitpW5FdcAS/qx42bWZxU9k+vqnlz5tM63AHeQy7pzDPBIw+tNtHgQnpkdbEHU8ve4p5N0kJN0OPAN4I8i4uniEeuFiAhJPYX5MuvPcoDh+S/tpSoz61aFH7WUatqRNEIR4L4eEf9cLn68IalEu6w7myme0z7h2HLZPiJiRUSMRsTo8BG5llAz66Ood56moUzrqoCvAPdHxF81rMpk3VkNnCppbtngcCovzKFoZgMggKhHx6lXkn6z7KVRl9Q2taGkJZIekLRB0gUNy4+XdEe5/HpJMzttM3Mm93bg94B3S7qrnE4nkXUnIrYBnwXuLKeLy2VmNkgipupM7l7gN4Db2liHcnIAAAM2SURBVBWQNAxcDrwPWAScXfboAPgccGlEvAZ4EjindS3PyySX/gHQrjdrJuvOSmBlp+2Y2cE1FQ0PEXE/QOM9/RZOBDaUCeqRdB1whqT7gXcDv12Wuwb4DHDF/iobyBEPz/380a0P/c6FDzctng9sPRj7c4BU6XiqdCwwgMfz8y7KDh+9z6JWx/OqxhfP8OTq78aN8xPVHyJpbcPrFRGxoovdy2jVK+Mk4EjgqYgYb1jesbfGQAa5iHhZ8zJJayOi7TX8dFOl46nSscCL83giom/9VyV9F3h5i1UXRkSre/cH1EAGOTObviLilB6raNcr4wlgjqQZ5dlc294ajXKjg83Mps6dwMKyJXUmsBRYFREB3AqcWZZr16vjBaZTkOv3df/BVqXjqdKxgI/ngJH065I2AW8Dvi1pdbn8FZJuBijP0s6n6G52P3BDRKwrq/hT4BOSNlDco/tKx23GNB2PZmaWMZ3O5MzMuuYgZ2aVNvBBrt3wjulK0kOS7ilHjqzt/I7BImmlpC2S7m1YlnqA6iBqczyfkbS5aYTPwOv1AbdVNdBBrsPwjunsXRGxeJr2xbqafZ8J2PEBqgPsalo/4/DS8jtaHBE3T/E+TdakH3BbZQMd5GgY3hERzwHXUTys0w6SiLgNaB5/nHmA6kBqczzTUo8PuK2sQQ9yVXzoZgDfkfTj8hl6VZB5gOp0c76ku8vL2Wl3eTeJB9xW1qAHuSp6R0S8leIS/DxJ7zzYO9RPZYfN6d4v6Qrg1cBi4DHgiwd3d7rT/IDbxnUV+X66MuhBLv3QzekiIjaXP7cAN1Fckk93mQeoThsR8XhE1KJIRPp3TKPvqIcH3FbWoAe5lsM7DvI+TZqk2ZKOmJineIjovft/17SQeYDqtDEREEq/zjT5jnp8wG1lDfyIh7L5/kvAMLAyIv7yIO/SpEk6geLsDYqHI/zjdDseSdcCJ1M8vudxipST3wRuAF4JPAycNV0ejtrmeE6muFQN4CHgow33tAaWpHcA/wbcA0w84fLPKO7LTcvvpx8GPsiZmfVi0C9Xzcx64iBnZpXmIGdmleYgZ2aV5iBnZpXmIGdmleYgZ2aV9v8BfII+ystgMIcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the scatter plot \n",
        "plt.scatter(df['income'], df['age'])\n",
        "plt.xlabel('Income') \n",
        "plt.ylabel('Age') \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "HfMN0tcKCAqy",
        "outputId": "2801ad58-c7c9-49a9-cef9-54a2a3838eaf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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GGBvtOck5NkVAXPuSUSBKbcNPYAwmo9TjOeXou+b0xxlDGuNWnduzqIB/+1//0Wj+luZmq07ZuBXAEz/jhI2ubtKbTdq7rT4dtwiIrB3TxjQZt92wMpWNTMYiKlNlax077WLraV4ja0NWBP3izBymy/PSa2wL2buSxYa0VoYnfsYJG13dpDfLtPdg/nbRp+MWAZG1ozM423ztFXWW00FWT1J9HbAoohL5Xac9p11sPc1rZG3IiqDrsC1k70qabeWBTDdwpQVv4GodWs3EqtXiiYOt+V0Y1fjydj9cx366BcfQyjRlAxeTP0x50aq8Z1PR9Ggfu7/2JM75lZtKRQ+7brm+7tuVTY62Kh4BYOOfPJJZUfU0C4XHKaKiKjyjKiAi20chiz+tcdm24zr2ds6tbySFXbt2NTsGI3v37t21ffv2ZofR9gT68OtTMwCANy7N4ttPn8XVvUWsXfEmAMDyJYvw6MlXEM3GnJsTWHVFT/U8XR/D+8drCrhcmp3Ho0+9UnO9TSxBPN9++ixmJemhl8rzuDQ7r70+Drax2aIbg1cgFIhq7nfRK+Dum6+r6SuIaWauXhuPnq+K/0c/mcanDz2TeFwu90c3dhlpvYadwu7du1/etWvX3uhx1viZKjb6sEqXLc/b6eojB0/VFduQXW+rVYe1WRNp5WWnXSg8OoYgp9ylgIqugEj0fFX8Dxx9MZVxudwfmbZ++6ZVKHryqamdc+sbCUs9TBXbvOjzEjsC3fW259jk+suOBznsNnpxGnnZWfi72BQCjxPTvBB116rOVWUVpeV5pDouG/s9Q+uUr2e75tY3Ev7Gz1SxzWVOkvNsmxuuOk+3lpDW/oC4baiON8Ir3yUm13uQ1vnNaoephyd+poptLnOSnGdV7rbXRfW5/pLzLlxS5+ib8vHTyst2GX+aBdXTisll30Kce5ZWTnyn5dY3Ep74mSq2ucxJcp6HNvZjZOt69Ia+uZeKHka21RZdj7OWEI0rq6LqLuNPez0gjZh0/jRAZV0gyT1LKye+03LrGwnn8TMtS95y0mW08hhaOTYmHTiPv8NJM+88aQwTk9Mo+PnmUf/9MGn4zNvEk+U9iTOGRr1WWd7fZvn7M3aw1NMBNEpnto0BWMgg0cVi4+2fRjytor03Mq44sdnSLH9/xh6e+DuARunMrjGYYrHx9k8znmZr742MK05stjTL35+xh6WeDqAV6oqa+pI9n2XcjbwnLl75jX6tkvj4q2iWvz9jD0/8HUDWWrkNqjq0uliS1Ns1oWp7sdeFzXsOObeZVJ8O/ItMHvR5QPe6qe5TK7xHOwmWejqAZudDj45N4OLMrPJ5l3xz23q7JlRe/tPleec2k8YS+BcFpnVR8pa7rnrdtqztU96nZr9HOw2e+DuAZudDq/x5YIglSb1dEyov/yg2bSaNRXd/ZF47rY7qdTt88qzyPjX7PdppsNTTIWSh5dqi0mkJwGM7btRem6TerolJxTds1zaTxqI7T+a1kwdkr5uprm0z36OdBk/8bYQqT37L2j4cPnk28/zotPVbnW6uarOLyNqz3dYLXufBM3LwVCxdfufoCTxw9EWlMZqsjazy3F3atT1Xdl6px5PKWS61HJh0YKmnTdDlyd9/5Ezm+dE6nTuOfhunti9QGbft+Gw8a2w8eFyuAyqT/v1Hzhgn/bB/UVZ57i7t2p4rO29437jS1VXnv8RkA0/8bYKpbmuYLPKjdTp3HP3WtravzG/GdnwqL/i4HjwBpvE9cPRFY2xR/6Ks8txd2rU9V3ZeeV7UFe8JP8f5+o2FpZ42IS3P9LT7j6vf2ujmQxv7jbqxibi6cpJ1C903fVVN2azy3F3atT03Tkycr99Y+Bt/m5DE6zwNv/gk3umy/htRGyAOQaxJ8u11rpgqshpnGj7+wbpKkpg4X7+x8MTfJrj414T157S047h52Kr+t6zty7w2gCtJdP0wm67pdToOZDdOFz8k23UV2XleF0nrKwCcr98MeOJvE1T+NQUirW6dlnYcNw9b1f/hk2czrw3gShJdP8zp1+QfHKrjQHbjdPFDsl1XkcU6sm09Rraul9YV5nz9xsMaf5ugq7l6z9A65+viaK5x9HJd/7btNSr/O4mub9OO6Z63gq+O7bqKKlae4FuDTCd+IjoN4A0AcwBmhRCDRHQFgAcBDAA4DeCDQohzWcbR6qSRn63ywjFpp0k8UtKIW9V/qcfTeuY0w7s9LT+ZLO553PsRJxb21ck/jZB6tgghNoSqwOwA8KgQ4ucAPOr/3rGkobGrvHCidWxlpK3Np7E24BUIFy7NKttulnd7s2vJqsa9c/RE7PsRJxb21ck/zdD4PwDgPv/xfQCGmhBDy5CGxq7yelm6uNv4rS9tbT6NtYEli7pRjiR9h9tulnd7s2vJqsb9wNEXY9+POLGwr07+yVrjFwAeISIB4LNCiL0ArhRCvOw//yMAV8ouJKLtALYDwKpVqzIOs3mkobGrzrX1oklbm3cl2v/qHQe0bTfTuz0tnT3Ne67aF5DlXgb21ck3WU/87xJCTBDRzwD4JhGdDD8phBD+h0Id/ofEXqBSbD3jOJtGGj42Xb4vT5RlRU96fho+81npvKNjE8rxBG03qo6t7BoATakLq7svBcVxAeDau/4Bt92wUrvAnzQurpObPzKVeoQQE/6/rwL4MoB3AHiFiFYAgP/vq1nG0Oqk4WOj+sZ3cabigRJXE0/bf8dE0J9sPOG2G1HHVuU3M7x/vOFrC6b7ctsNK5WeQ3NC4P4jZ7Bz9ERmcXGd3PyR2cRPREuI6PLgMYD3APg+gK8CuMM/7Q4AX8kqhjyQlo+NjPJcxQMlriaetv+OCdW4op70jahjq/Kbia6lNGJtwXRf7hlaV70fKmz8gdKIi+vk5oMspZ4rAXyZKhs1ugH8vRDiG0T0XQAPEdFHALwA4IMZxpAL0vKxcT03qc982jqvbi+CbONWlnVs07rHaWBzX4L7MaBYHzE5gaYZF/vutD6ZTfxCiOcArJccfw3Au7PqtxOw9ZEHKvnwk9NlyMxlBIDNew5JdVmT1p62tmuj7du0IdPkVe3qfOBd7rEAMLDjAEpFD7tuuT51j3yX+6LS+wu+n46sXkPc147z+fMLWzbkEFW9WBnnpsrQfdmT6bImTVlXOzUOttq+TRs1mvz+cQzvG1d+29X5wLv6zQDA5HQZw/vGU/fId7kvt92wUtrmpmt6lfUa4r52nM+fX3jizyG29WJtieqyJk1ZVzs1Drbavmsb5TlRtx+g5nmND7yN34xrm7bo7odpTeOeoXW4fdOqqhdO4NV0+rVp5bpQ3NeO8/nzC3v15BTbHH1bwrqsSVNO6oFve51Lvdm0+wb0fjOrdxxQWjNn5ZE/LwSeV/j1h7lnaF1d+qZqb4SpTxOcz59PeOLPKS4atG17prZNefSlHg8bdj9S9QzqImBeQKkj29asldWnjbYZ937E1aN1/Zlq9JrWRdKoJxzt07S020WE1TsOVOM69sLrxnvO5BeWenKKTb1YW6K6rEm7VfnrnJ8q1xjFBSqLbh3B5G2vqk8rq8Fru+6hGrcLwzetker9Kn8kl5z3NOoJR/s0MSdENa4/3Ddudc+Z/MITf07R1Yu1QacXm7Rblb/OvKY/23UERPrT5Z9HPeBV6x7BWEtFD709Xip69NDGfoxsXY/eUGZQtE5uGJec9zTqCav6DAivAUSZ06yLcJ5+e8BST45R6as6/TnApBebtFtbf50wNusIUW97U/55uB3VuoetNu5KlnsJ0lhL0d3jZ+99LwC71y1u/0zrwhN/m6HLhw/jmotv8q2x7TP82MaHnyDdglDXpm7cYW3cdsxp71MwrZvI+nPNkw+3sazogQjSVF6be2YaC5NvSGSwoy9tBgcHxbFjx5odRssTaLomO4eiV8Cvv70fXzo+UXNu0StI5Q9Zu14XAQSpHbSqz3Db0jYLBAjUpGAWukgpPQRtAjCO22XMsthU98YWXZuy+NN4jWTY3DObe84LvPmAiI6HaqFUYY2/jbDx8Am0bZdcfFvfGgAIS8bBWqvtOoLMh39uXmDJokKdFh1u02bcLr71WXjQ6NZNktYdVsUcJbwvQrdX4M+2ra/ZCxDAefrtA0s9bYRJew3r5y76sZOmK4DTlnq67TrB1MycVqO3jc/Wtz4rDxrVmkAadYdtYgvvizDtnRja2J+ZlTPTfHjibyNMuewuufphSj0ezlluGAtf76qTx62/a5vDr/KxiY45bQ8a032IWy/ZJuYwy4oL9zGpLxKTb1jqaSN0uf2uufoBo2MTuHCpvp5vF6Eujz18fRyv9jj1d4PrTDn8XhdJfetlY07Tg8Z0H5LUSw6zZW2f9nmvi3BxZuE+JvFFYvIPT/xtRFhHBhZytOPk6geMHDwl9bt502Kv6lsjuz6OTh6n/m5wncm7aOni7hrfet2Y0/SgMd2HJPWSwxw+eVb5XH+piKWLu6X92Pj/MO0HSz1thktuuc25Ki34/HRZe31cndy1/m6AybsoeN72/qTlQWO6D0nrJZv6CdZ1VPcxqz0OTGvDE3/OkHmqx/FWT+obY6pvm5aGbKt/26xvuK45pJHLH9f3KPD4LxBh0zW9OP3atDaOuP2wpt+ZsNSTI6LeK8HE6uqtntQ3xqa+bRoasov+bVrfcK0hkFY92Ti+R2HmhMBjz75ujCNOP6zpdy488ecI23z1JLV0o6RR3xZw89YPt2Wrf5vWN1xrCKSVy+/qe2SDLI44/kqs6XcuRqmHiK4E8N8AXCWE+DUiug7AO4UQn888OqaGpB4tpud1vjFJPWlcvPVNban0b12crr43aebyu/geqWrm2sTh6q/EdC42Gv//BvC3AP7Y//1pAA8C4Im/waj07ii6WrIAUPS6MFWu99LU1eBVEdXBVTGaYpK1afLpV8XgooGHc9tt6vTG8cQPYtz9tSer+yGC+rzAgt/RsqL9PWJtnkmCzcT/ZiHEQ0R0FwAIIWaJSK83MKkzOjaBN35ar3fLCGrJyiannaMnpJN+QKAhAzBOblF/mInJaXgFqhZgsY1J12aUqC4ti0EW//BNa6R+QxdnZqsfVEGdXgj1Lt/AEz/avmlMw/vHa2Sryeky7nzoCRSIqumqNh/qAaa8fYbRYaPxXySi5fBNEoloE4DzmUbF1DFy8JTWJz2Mru6rzt8+wFbLVtW5lUVpW4vW1qdfd76tBi7LbTfV6VW1bxqTbK1iPmJI54Iub59hTNh8478TwFcBXEtEjwHoA7A106iYOly1ZdX5tha8Nv2pzlF1kaTNqE+/6XwbDTyOF72p36TnNrNNpnMwTvxCiH8hon8HYA0q//9OCSHSrfTNGHGtKRvOWw/n/LtcD+j18zgxxW0zWhM28NZPosUnqVucto+OKy5rJgwTxSj1ENGtAG5BZeJ/C4CbiejdRPQzWQfHLKCq8SojmrcO2H/TD64fvmmNMZfdpe5v0jbDNWHvevgEdo6eUO4XCM6P6w1k8v1xzX9XvXZdBOc6wQHBmgnDxMFG4/8IgM8B+LD/8zcAPg7gMSL6zQxjY0IENV4lJVIB1HuuyPLWVddtvvYKJ5/4cJ1bVW1YWUwubQbxqOrOyrz1Zee5egONbF2PkW21HkRBLeO4+e+q+ryf/OCGmr7CNYFLRQ89nvq/p+2aCcPIMFbgIqKDAH5LCPGK//uVAP4OwG0A/q8Q4q1ZB8kVuBZQ1dMloMZzxVR3N3p+Fn3J+nA5V3e+LTbjbHVc7xnDBKgqcNks7q4MJn2fV/1jrxMRa/0ZodLBbTVwF29+3Tkqf/zRsQns+uqT2hTEwG8GAHp7PHzi5uu18cs0+aT6eByPnrhk1Q/77DBpYyP1fIuIvk5EdxDRHQC+4h9bAmAy2/A6E50ObquBb1nbZ+3Nr0KlTZ+fLuPOB59wyjs/N1XG8P5xZVwqTd5lHSFKHI+euKTl7SODfXaYtLGZ+D+Kys7dDf7PMQBCCHFRCLEly+A6FZ0ObquBh+u1AnpvfhVDG/uxZFH9H4XzAlBvAVNTnhPVuFRxm/LvdesJMi3e1aMnLlnU6Q1gnx0mbWzSOQURPQdgE4BtAJ4H8KWsA+tkTLnp4Xx0nV99Gt4s5x2+1dsQxOXinR8A1g8AAB78SURBVGMz3nkhpDViXT164pJVnd4A9tlh0kQ58RPRW1BZwL0NwI9R8ech12/5RFRA5a+ECSHE+4loNYAvAlgO4DiA3xRCzMSMP/fIdGEXTVfnQZNGXEkWVmUEY+hZVMDFmfqsHFm+fjgmnc9/+F4uK3oggpXfT9RHB1hYk7CdbHV1A6Lt93hdWNRdwPnpMko9HoSofMBmuf7AMGF0Us9JADcCeL8Q4l1CiE8DiOPR8zEAT4V+/+8A/lwI8bMAzqGSLtqRqHRhmQ6u0nRV9WYvzsTP8476/qeFV6j46O8cPSGd9IH6tYpwbVqdz39Uy5+cLmsLxAdeN4GPTvTcYE3C5h7q6gZsWdtX1/5UeR6T02UIv5/gcVbrDwwTRTfx3wrgZQCHiehviOjdgLVlOACAiK4G8D5U9gGAiAiVD5P9/in3ARhyDbpdUOnCYX3epOmq6s2W5+Lneev8clQau+yc8Jm9PZUavUMb+638goD62rQ6n3/bfQsBgdeNykcHsL+HuroBh0+eVbYvI4v1B4aJopR6hBCjAEb97J0PAPh9AD9DRJ8B8GUhxCMW7f8FgD8CcLn/+3IAk0KI4OvRSwCkf9cS0XYA2wFg1apVFl3lD50u7KLpqvzp4+rLOr+ceYsdwPNC4LQmv9xlF7GpNm3g86/S8uO2Gz0vzjmTU2Xn2rm2fTJMEoxZPX72zt8LIW4GcDWAMVR27mohovcDeFUIcTxOYEKIvUKIQSHEYF9fe1rQqvKwZcdHxyawec8hrN5xAJv3HKqRA1TtBHp59HwTKh+Yq0pFq9xx0/qCxR8NNX0G2r6MIP/fNac9XIvW5rw459jerzh9MkwSnEovCiHO+RPyuy1O3wzgFiI6jcpi7o0APgWgRETBXxpXA+hYQdM2PzstfxtbvfrCJYle7evzNnn1uvWF0bEJpV5YiKxVhLV7kyePbt9ClGgt2mi/4XhscuVV3vhb1vY5eSyZ2mOYtMis5q4Q4i4hxNVCiAEAvwHgkBDiwwAOY8HWOdgQ1pHY5men5W9jrVdLPOKXLOquyk86fx5Ar42PHDxVV6QFqGS6/FnEI8dWu5ftW1Ahq0V7+WVyxfPyy+pr+8pQeeMfPnlW6tPT43WhpPmriL32mayxsWxIm48D+CIR3YOKbNTRJRxttHybHHHb3H4TqnPC+fw2mrprXvt0eV56L2y1+/C6iM7bRubpr9qrYLuHwWXfRRhVnKzxM1nTkIlfCPEtAN/yHz8H4B2N6LfViOvl4urVksTbxXStbX6/qi9dvntAuI6ALeHrXfY2mPYGRGMKXrsta/tw+ORZ/HBy2up6VczswcM0g8ykHqaWJF4url4tSbxddNe65PfLdGpdvnsQW5w9BNGx2e5tMO0NkMUUvHb3HzlT/V21/mDS6tmDh2kWPPE3iCReLq5eLUm8XXTX6vL7o8h0al2+exCbqQ+VJ094bLZ7G0x7A2xjUmHS6tmDh2kWzdD4O5KkXi6uXi1JvF1U1yatM6vLd7ftQ+XJo2tTFYNpb4BtTCpsrmMPHqYZ8MTfINLSc+OsE6iucW3LxRvfxVco8O0vECk9fALCewyS1u61WW/QtWWCtXqmVWGpp0GkoefGWSdQXRPUrHVpy9YbX+crpLt+TgjtpA8s1Jo13QuVvh7e23BxZrZuLUAWe5yaAKzVM60MT/wNIg09N846geoaWc1alxq1wMJ+gd4eD6WiZ+UrZNoDYCKoNWu6Fza58OU5gaWLu42vSXTcMpYsKrBWz+QGlnoaSFI9N846geo5VSaKSZdOOoY4vjpRbMZrq8tPTpUxdvd7jOeZ9ghMzczhyT+p3yPAMK0IT/wNJlqrtrfHw/t+fgUOfO/lqnVvqehh1y31XvAu6wSmfPuCIvdcANi851CNXi7zuZ+csvePj2rxpR5Pa5lsYrHXhZlZIY0/7N1jo8uH9yeEPfNVr4Eq9jh6fqNqATNMFJZ6Gsjo2ASG943XLCiemyrj/iNnaiaTyekyhvfVe8HH8faRUfQKuO2GlUrdOqyXR7X0wOc+yRrDhUuzWv8alXdOwHR5PhXvnvD+hKhnvuw1MPkYuZBljV6GMcETfwNR+eDICLTsMEm8fQKCa+4ZWqfVrQO93JTDHmeNoTwvsGRRd13fQY5+2LPH8BmgjElW00CV/6/aXxB9DUw+Ri5kWaOXYUyw1NNAXPPBTfVnXfuJetWYdGvbeOOsMZyfLuOJT6i1dZPvkE1MtusRtvHb+Bi5xOcaC8OkBU/8GRPWcYkAhxokTrrxztETeODoi9oiJ67ePioPmihhD5yobq3KlQ+0eNPEHDeHvuip/5i1jTHo37ReEtdzn316mGbBUk+GRHVcS5UHQK1/jYmdoydw/5Ez2kna1dsHsK+UFXjgyHRrWa580LaNph0nhx6o1LXdOXqi7rgqRpmkFNTMNa2XxMnXZ58eppnwxJ8hNh4vRMDtm1bV+LWXih5Gtq231o11NWzjePu45tkHHjhSPd/PlY9bI0C2rtGj+TYfRnZfVDEuK3rS10BXDyBJvj779DDNhKWeDLHSawVwz9A6K/8ZFbpv5s9rat+GsfHz16Ebq67ubBw/G9v4ZPdF5xcku1eqPQcqb38X2KeHaRY88WeIjT4d9o6Jm8dNgFJ/tm07jgd+mECbdl0r6FlUwLV3/QPmhECBCLfdsLLuQzDuPgDZXxm22noWuj7DtAos9WSIjT4dpy5umNGxCXRpch5t2nbxwPe6qC4HP9CmXdcKCl2EizNz1efnhMD9R87UaPNx9gEE3HbDyrpjuvq4sj5lsBbP5B2e+DMkquOGPW2S1MUNM3LwFOYsVo11bdv6zReIMLJtPUa21tfGDdfjtVkr6C8VMa+IO6zNm/YBBDFsvvaKan/BfgCZfKarj6vrMxw3a/FM3mGpJ2N09VZlpJHr73qubRthn3rdQrFprSDQxwcUz4f/Qoi7D0CFzT2w3QfBMHmFJ/4GY6sd2/q4JPXId2nDVddW5ceXejxs3nNIeV34LwUbX33ZvQmvWQS+RP2a9YHwvgLOsWfaHZZ6Goitduzi4yLT1b0CWfnM69pQxWaLqr5uF1U89XUfNGFtXrduoLo30fsc/AWhWx8I7yvgHHum3eGJv4HYascuPi6yfPCRresxsk2uw8tQ+ewH/8atHSDzvxGA0q9Ips3brBtE743uPgfrA7p2OMeeaXdY6mkgttqxqxavWkdoVI1eGapYVVsOCMCz975X+pzNuoGNRh+g89YJruUce6ad4Ym/gai040DzNnnHCFTsGZJs9sqCsE9QkIvv6rGjyqOPrnHY+OGb+hZQ1yNgHZ/pBFjqaSAqPT7QvE3+NgDq8tybTdQnKMjFH1hetPbYiernujrBNn74tvsnTHEwTLvCE38DkWnHSxZ112negb+NCp03T6NRxXLkuXPGOrWAXD/X1Qm28cNXrVnIKBCxjs90HCz1NBhb3xmdv42ta2Yj0FXCMvn9q/LiXesEyzR72X2WXT0vhLWfEcO0Czzxp0Tc+qlxvPCDb7CtULNVpZUDwMCOA9rcedd9Baq+lhUX1khKPR6EqHwYhO8J5+YzzAIs9aRAkvqpcbzwb7thZcvUbN10Ta/2eVXuvOu+AlWdYK+LcHFmYY3k3FQZk9P1NYE5N59hFuCJPwWS1E918bcJ57m3Ss3W06+ZM3dk3jou9QFkdYKD40sXd0v3CwRwbj7D1MNSTwok9cCx9bcJ57m3Ss1W2/5cvXV0exNcvfk5N59hasnsGz8RLSaix4lonIieJKLd/vHVRHSUiJ4hogeJaFFWMTQKnVadVVtp9pkE2/6yisumXdbxGaYWEhlliBARAVgihLhARB6AfwLwMQB3AnhYCPFFIvprAONCiM/o2hocHBTHjh3LJM64hBdWSz0eLlyarUk1LHoF3HtrZaOVywJsoN2rLAdKRQ+7brkeADC8f7xG5vAKhJGtdiUbo/HLFkRt70M0jihFr4Bff3s/Dp88W70PW9b21fweaO029yoc+2KvC9PleW3fLOkwnQoRHRdCDEaPZyb1iMonygX/V8//EQBuBPDv/eP3AdgFQDvxtxrRyfncVBlegVAqejWTJ4Ca84LFRkBvawxAWQ1rcrqM4X3j+NA7VtaX3bL8DJfFH2AToyze3V97Upq50+9P8l86PlFzH+4/cqamz+H944BY8PFRxRGNXTbp9/gfBs3KdGKYViezb/wAQEQFAMcB/CyAvwIwAuCIEOJn/edXAvhHIcRbde202jf+zXsOSSfl/lKxJi/d9jzXfgB1aqNN27p2XWO0waY/2zgaHTvD5BnVN/5Ms3qEEHNCiA0ArgbwDgBrba8lou1EdIyIjp09K6+a1CxsF1aTLsDqzlOle9q0ndY5tiRpy/aeptUfw3QCDcnqEUJMEtFhAO8EUCKibiHELCofCNLEcyHEXgB7gco3/kbEaYtqM5BA5RupzaahsLFZQLhgyPBNa5yNzoK248bv2o4tccahiqPRsTNMO5JlVk8fEZX8x0UAvwLgKQCHAWz1T7sDwFeyiiErdCZgNpuGBpYXa4zNAsIFQ+56+AQGlrtNYLYbkkwmZmlvbLIxTbMtHtPo2BmmHclS6lkB4DARfQ/AdwF8UwjxdQAfB3AnET0DYDmAz2cYQyZETcCimDYNHXnunLGP6fKc1XkBLhuSdEXgs9jYJLsPt29aFat4TLStUtFDb092sTNMO5Lp4m5atNribhidAZnK/EtVZDwuur4YhulcGp7O2SnYmn+Fc89t0Rmg6fqK9hc3rTHahiz3XpVnv+urT1aLyfT2ePjEzdcr+3c9n2GYZLBXT0JszL+ihmo2eIVKJStVQZbweVFNOw0DN1kb9x85Y2xzdGwCw/vGayqInZsqY3j/uLR/1/MZhkkOT/wJsTH/0hX/VrFkUXfFjG3bepSKXvV4+GOgt8eT7tRNw8DNJmZZmyMHT0mLpZTnhLR/1/MZhkkOSz0pYDL/0hVZB+QbboPiInGMxdIwcIu710B3new51/MZhkkOT/wNQFWIRCf7CFQWgYPi5fcMravTwoEF757wh4Nun8HGP3kE56bKdXsGguuDPmwlKaLKAneg+evy7GVrEa7nMwyTHJZ6MmZ0bALnNWUUTQTFyz/8N/+vTgsHFrx7wnr48E1rlGsDwQdQdM/A6NiEVG83MS9Qo/lvWdsn7Vu2FqGLVXU+wzDJ4Yk/Y0YOnoLaO9Kex559XaqFAxVjs7AePrSxX1usPUqg1av0dpd2Dp88W7cuoVqLCGJ1OZ9hmOSw1JMxLjo1wdpg09iPrli7zfVx+eHktPO6BBdIYZjGwhN/QnT58qNjE9qi6VGuKhXxo/OXrM8PEy44flWpiGVFz0myWVb08Mal2Vh9h1Hp8q1QGJ5hmAo88Scg6g0f9pAHKl78thNpkPt/7IXXa7zqbegCcHFmtjrRT0xOwysQuqiiwZsICpYnnfRVPjm6+8STP8M0Hp74E2DKl1flwQcTsiyzJpgIo86dKkpFD0Soyxoqzwn09lR0c1lGUUB/qYipmVntOTZEs4PC6O4TT/wM03h44k9AnHx5AvDcvXpfnXuG1uGeoXXWPkCqguOTU2U8v+d92nYe23GjtsA7YF53CNpR0SqF4RmGqcATfwwCvVo1IV5VKuLiT2elGrtNbrqpfSJgw+5HqmUeVfsEgr5UufKlnsq6gG4cAIz+99H1hWj9XBWcp88wzYHTOR0Je9jIKHoFbFnbh4szs3XPeV3m3HRT+0BFJpqcLlfz5y9cmoVXUHvZy/yEvALhwqVZ7TiGb1qj3RMQjOnizGyNh8/wvnEM7x+vHpN9sNjcC4ZhsoG/8Tui87AJdO6Rg6dQnquf7pYu7jZq2nF8fcrzAqWihyWXdUuzZsIF3IPnVX+RhMcRjjW8YzhYo1CtD9jsBbC5FwzDZANP/I7ofHcCnfsPHnxCeo5Nbn1c3fv8dBlPfOI9yuejufI6XT+q1+vy7FXtmHDdZ8AwTHrwxB9Blm8OLHxbVuXlh/VqW49+Wd9x6SKq8cwxfZtWxbgstIPWxo8/bj1d1vcZpnmwxh9C5kE/vH8cw/sW9GpViuWWtX3VxzYe/bK+h/eNx965OyeEk/e+Sru/ODNb9e2x8ePfsravfv2gi+rWHMJwXVyGaS488YeQ6evlOWGlWR8+ebb62MajX9a3rU9OuEZugeonWBvvfZWfT+CDb+vHf/jk2bqxjmxbj5Gt67kuLsO0KCz1hEiSVx691tV/xrZvAjB294KWr9LYbdpT6eyuvv2qsfLkzjCtCU/8IeLq1UBFYx8dm6jztZdlwgQyR9Rb3zZGm5jDWn0QT3TtQpf/b7ubl7V6hskfLPWECOv0rswJofW1D1ScYN3gzgefcJ70ZTGatHpAvXahmtgHlhdx4VL9PoQonIvPMPmEJ/4QYZ1eRX+piNs3rdJq6ya9vjwnYnv0R2M0afWAeu1CxZHnznEuPsO0MSz1hDBp2+Ec9y8oHDSz9p+RtW/S6l1jsnXp5Fx8hsknPPH72HjnB7nyy3xHTNmptv42cZH54qh0/i4iDMTcYGUD6/sMk09Y6sGCBm76phvkyk9Ol6U+97b+Nkl446ezVrn0QbwqCl2U6MXnXHyGyS888SOeP06UAlE1Pz2oI5vF1D8X+cSR5dLL1h+iXH5ZNz75oQ01tW5VBOsaLvsSGIZpXVjqQTq6/LwQNRPh0MZ+pWdP2kRz6W38c85Pl+uu0Xn2MwzTPrTtxO9S4zVJ/n64jWjufjZiTz0CwOY9h6pjtBlPNM8/rr8QwzD5oy2lHlneus6/ZvimNVpvGROBB380d1+lsKv09S6CNg6dB054jDKvoCjhPH8gnr8QwzD5pC0nflMt3ChDG/uxZJH5j58CkdJ75vDJs1a570TAn21bX6ev9/Z4+OQHN9R43IQ9eaIeODLCdWwDzV9FOM8/uAeu/kIMw+STtpR64tR4PW+xi3ZeiJpat2Gs9Xyx4GGjmlRNk+3Qxn6lJh+M0Ua/T+ovxDBMPsls4ieilQD+DsCVqKgee4UQnyKiKwA8CGAAwGkAHxRCnEuz7zh6tY0uHr5+5+gJPHD0RevNTgGBpw8A6RpEdJ2gt8fDJ26+vm5CVsUrAAzsOFD5S4Eqm6xU+xMEamv32vj4MwyTf7KUemYB/KEQ4joAmwB8lIiuA7ADwKNCiJ8D8Kj/e6rE0attcu8Dn5ydoydw/5EzzpM+UMmtj9akDfT5naMn6tYJzk2VMbx/vG59wqTjT06XcW6qrK0hEJzn4uPPMEz+yWziF0K8LIT4F//xGwCeAtAP4AMA7vNPuw/AUNp9x9GrVZ43YQKfnAeOvmgVx5JFBWlmT3le1HnlTJfn8MDRF6XrBFE9Poj33lvXWeXs22Lj488wTP5piMZPRAMANgI4CuBKIcTL/lM/QkUKkl2zHcB2AFi1apVzn3H0apP3TKCJm77pE1BdC3CpSatrV7Y+kcVegay9hhiGaT6ZT/xEtBTAlwD8vhDiJxT6hiqEEEQkne2EEHsB7AWAwcHBuBUJnTDp/EHue8Hg6eMVCNfe9Q/OUpCu3fD6QniPgslfyBXO22eY9ifTdE4i8lCZ9L8ghHjYP/wKEa3wn18B4NUsY3DBpJsHue+brunVtjMzJ/Rmb4rjm67pla4zeIUF3/voHoU0J33O22eYziCziZ8qX+0/D+ApIcQnQ099FcAd/uM7AHwlqxhcMeW/B1r76dfiySHBesOyHrk/zunXpjGybX1dfv/I1vVV2SoNX6Ew4X0CnLfPMJ0BiRS/MdY0TPQuAN8BcAKo1h35L6jo/A8BWAXgBVTSOV/XtTU4OCiOHTuWSZwqdN41gHpXro7TId1f1bZqn4AprjjY9McwTH4houNCiMHo8cw0fiHEP0FtV/PurPq1wcbHR+dxv9jrwsUZ92/dAzsOoECknLgDv58gtlKPByFQk2evqpMbB9bzGaYzaUvLBh22Pj4qvX9OiFiTfvh6GYHfTzi2c1Plmjz74f3jOJ/SpM96PsN0Lh038dv6+CTJk19UIKfrwn4/Ov3epVZv1EM/6vvDej7DdC5t6dWjw8XHJ26efHlO1OTx6zT5sN99Wjn57KHPMIyOjvvGr9K1XY/b9mG63uXcOP0zDMNE6biJ39XHx8bbXteW7nqXc4FKPr/JT4i1e4ZhTHSc1BPOh7epziU7f8vaPhw+eVaZeRMtwRhcPzE5Xd2d2284V9W2rC1dmwzDMFEyy+NPk2bk8TMMw+QdVR5/x0k9DMMwnQ5P/AzDMB0GT/wMwzAdBk/8DMMwHQZP/AzDMB1GLrJ6iOgsKk6ercybAfy42UGkTLuNicfT+rTbmJo9nn8jhOiLHszFxJ8HiOiYLG0qz7TbmHg8rU+7jalVx8NSD8MwTIfBEz/DMEyHwRN/euxtdgAZ0G5j4vG0Pu02ppYcD2v8DMMwHQZ/42cYhukweOJnGIbpMHjij0BE/4uIXiWi74eOXUFE3ySiH/j/9vrHiYj+koieIaLvEdHbQtfc4Z//AyK6I3T87UR0wr/mL4li1HZ0G89KIjpMRP9KRE8S0cfyPCYiWkxEjxPRuD+e3f7x1UR01I/hQSJa5B+/zP/9Gf/5gVBbd/nHTxHRTaHjv+ofe4aIdmQ1lsi4CkQ0RkRfb5PxnPbfE08Q0TH/WC7fc35/JSLaT0QniegpInpnnscDIQT/hH4A/BKAtwH4fujYnwLY4T/eAeC/+4/fC+AfUal2uAnAUf/4FQCe8//t9R/3+s897p9L/rW/lvF4VgB4m//4cgBPA7gur2Py+1jqP/YAHPX7fgjAb/jH/xrAf/If/x6Av/Yf/waAB/3H1wEYB3AZgNUAngVQ8H+eBXANgEX+Odc14H13J4C/B/B1//e8j+c0gDdHjuXyPef3dx+A/+A/XgSglOvxZP0GyOMPgAHUTvynAKzwH68AcMp//FkAt0XPA3AbgM+Gjn/WP7YCwMnQ8ZrzGjS2rwD4lXYYE4AeAP8C4AZUdkd2+8ffCeCg//gggHf6j7v98wjAXQDuCrV10L+ueq1/vOa8jMZxNYBHAdwI4Ot+fLkdj9/PadRP/Ll8zwFYBuB5+MkweR+PEIKlHkuuFEK87D/+EYAr/cf9AF4MnfeSf0x3/CXJ8YbgywIbUfmWnNsx+bLIEwBeBfBNVL7RTgohZiUxVOP2nz8PYDncx5klfwHgjwDM+78vR77HAwACwCNEdJyItvvH8vqeWw3gLIC/9eW4zxHREuR3PDzxuyIqH8m5y4EloqUAvgTg94UQPwk/l7cxCSHmhBAbUPmm/A4Aa5scUmyI6P0AXhVCHG92LCnzLiHE2wD8GoCPEtEvhZ/M2XuuGxX59zNCiI0ALqIi7VTJ2Xh44rfkFSJaAQD+v6/6xycArAydd7V/THf8asnxTCEiD5VJ/wtCiIf9w7keEwAIISYBHEZFzigRUVBDOhxDNW7/+WUAXoP7OLNiM4BbiOg0gC+iIvd8CvkdDwBACDHh//sqgC+j8gGd1/fcSwBeEkIc9X/fj8oHQV7Hwxq/7Af1Gv8Iahdx/tR//D7ULuI87h+/AhVNsNf/eR7AFf5z0UWc92Y8FgLwdwD+InI8l2MC0Aeg5D8uAvgOgPcD2IfaxdDf8x9/FLWLoQ/5j69H7WLoc6gshHb7j1djYTH0+ga9734ZC4u7uR0PgCUALg89/mcAv5rX95zf33cArPEf7/LHkt/xNOINnacfAA8AeBlAGZVP+o+goqE+CuAHAP5P6MUiAH+FisZ8AsBgqJ3fBfCM//M7oeODAL7vX/M/EFkwymA870LlT9DvAXjC/3lvXscE4OcBjPnj+T6Au/3j1/j/eZ5BZdK8zD++2P/9Gf/5a0Jt/bEf8ymEsij8+/O0/9wfN/C998tYmPhzOx4/9nH/58mgz7y+5/z+NgA45r/vRlGZuHM7HrZsYBiG6TBY42cYhukweOJnGIbpMHjiZxiG6TB44mcYhukweOJnGIbpMHjiZzoWIrrQ7BgYphnwxM8wDNNh8MTPdDxE9MtE9K2Q3/oXAj90IvoFIvpnqvj/P05El1OlJsDf+v7pY0S0xT/3t4lo1PdmP01E/5mI7vTPOUJEV/jnXUtE3/ANzL5DRLn1GmLySbf5FIbpCDaiYnvwQwCPAdhMRI8DeBDAh4QQ3yWiNwGYBvAxVHy51vmT9iNE9Ba/nbf6bS1GZXfmx4UQG4nozwH8FipOnHsB/EchxA+I6AYA/xMVjx6GaQg88TNMhceFEC8BgG/5PICK5fHLQojvAoDwXU2J6F0APu0fO0lELwAIJv7DQog3ALxBROcBfM0/fgLAz/suqb8IYF+oyNJlGY+NYWrgiZ9hKvw09HgO8f9vhNuZD/0+77fZhYrX/oaY7TNMYljjZxg1pwCsIKJfAABf3+9Gxanxw/6xtwBY5Z9rxP+r4Xki2uZfT0S0PovgGUYFT/wMo0AIMQPgQwA+TUTjqFT7WoyKJt9FRCdQWQP4bSHET9Ut1fFhAB/x23wSwAfSjZxh9LA7J8MwTIfB3/gZhmE6DJ74GYZhOgye+BmGYToMnvgZhmE6DJ74GYZhOgye+BmGYToMnvgZhmE6jP8PRT1LAluupMoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "11a: There seems to be a positive correlation between age and income, this is because we see that as people make more money, they tend to be older or you could view it as people become older, they tend to make more money. "
      ],
      "metadata": {
        "id": "jAg0evKIGo5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#12: \n",
        "plt.hist(df['income'], bins=9) \n",
        "plt.title('9 bins')\n",
        "plt.xlabel('income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IJ9yP2fuGmNo",
        "outputId": "3e14f57a-c523-41ca-cb0d-d713a1fb2b14"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.hist(df['age'], bins=15) \n",
        "plt.title('15 bins')\n",
        "plt.xlabel('age')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "UyKRCKO3JuaP",
        "outputId": "265314b7-88ac-4b97-a5ed-202c26abade9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#13: \n",
        "df['region'].value_counts().plot(kind = 'barh')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "RcbQYrgzJ2bw",
        "outputId": "4c048fd1-795a-46a8-c04e-2406595b8c66"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4508dee490>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#14a: \n",
        "pd.crosstab(df['pep'], df['region'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "0wbzQdL3_hbX",
        "outputId": "ddc6a14b-ab97-4eae-c588-f276e63f8b5c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "region  INNER_CITY  RURAL  SUBURBAN  TOWN\n",
              "pep                                      \n",
              "NO             146     50        28   102\n",
              "YES            123     46        34    71"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da50fa2f-4420-4c8b-8059-08956300c4db\">\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>region</th>\n",
              "      <th>INNER_CITY</th>\n",
              "      <th>RURAL</th>\n",
              "      <th>SUBURBAN</th>\n",
              "      <th>TOWN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pep</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NO</th>\n",
              "      <td>146</td>\n",
              "      <td>50</td>\n",
              "      <td>28</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YES</th>\n",
              "      <td>123</td>\n",
              "      <td>46</td>\n",
              "      <td>34</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da50fa2f-4420-4c8b-8059-08956300c4db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da50fa2f-4420-4c8b-8059-08956300c4db 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-da50fa2f-4420-4c8b-8059-08956300c4db');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#14b: \n",
        "pd.crosstab(df['pep'], df['region']).plot(kind = 'barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "VN-3j5neAU0Z",
        "outputId": "6f2801cd-7697-431f-8bbf-4c1e1edb9c81"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4508d69d50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "UymhGJ00M5Vk",
        "outputId": "aff1877a-c7b7-4600-e4b4-053716437626"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age   income  children  gender      region married car savings_acct  \\\n",
              "0   48  17546.0         1  FEMALE  INNER_CITY      NO  NO           NO   \n",
              "\n",
              "  current_acct mortgage  pep  \n",
              "0           NO       NO  YES  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff0122e3-fce6-4ede-8d7a-3fd1e197c20b\">\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>age</th>\n",
              "      <th>income</th>\n",
              "      <th>children</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>married</th>\n",
              "      <th>car</th>\n",
              "      <th>savings_acct</th>\n",
              "      <th>current_acct</th>\n",
              "      <th>mortgage</th>\n",
              "      <th>pep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48</td>\n",
              "      <td>17546.0</td>\n",
              "      <td>1</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff0122e3-fce6-4ede-8d7a-3fd1e197c20b')\"\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-ff0122e3-fce6-4ede-8d7a-3fd1e197c20b 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-ff0122e3-fce6-4ede-8d7a-3fd1e197c20b');\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": [
        "pd.crosstab(df['region'], df['married']).plot(kind = 'barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Uq8TaRJkN_gn",
        "outputId": "3955cc67-7708-4a83-9214-46a7d779d3dd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4504655ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that married people are more likely to live in a city based upon our data. Maybe that is because more people live in the city? \n",
        "\n"
      ],
      "metadata": {
        "id": "pWQVXVE5Nt_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_yIKP_d7Cd_F"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}