01: Statistical_Thinking_Correlations.ipynb

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    "colab": {
      "name": "01: Statistical_Thinking _Correlations.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XJTG_gVjoin"
      },
      "source": [
        "## Note: Statistics alone cannot conclude anything. All the tests we do are based on relative likelihood**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnU_4f65k8K0"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=Warning)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVdiNt0IflmG"
      },
      "source": [
        "loan_df = pd.read_csv('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/loan_analysis_summary.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1n88zNxhTCM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "95b0bde8-d54d-4119-95f4-c5a71220870c"
      },
      "source": [
        "loan_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "      <th>default_total</th>\n",
              "      <th>dti</th>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <th>median_annual_inc</th>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>default_rate</th>\n",
              "      <th>actual_default_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>500</td>\n",
              "      <td>3585</td>\n",
              "      <td>3183</td>\n",
              "      <td>19.737013</td>\n",
              "      <td>13660.71827</td>\n",
              "      <td>55000</td>\n",
              "      <td>63396.10292</td>\n",
              "      <td>15.352285</td>\n",
              "      <td>2.463470</td>\n",
              "      <td>0.520266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>510</td>\n",
              "      <td>8223</td>\n",
              "      <td>7191</td>\n",
              "      <td>19.747810</td>\n",
              "      <td>13889.67226</td>\n",
              "      <td>56000</td>\n",
              "      <td>65446.52263</td>\n",
              "      <td>15.440212</td>\n",
              "      <td>5.565445</td>\n",
              "      <td>1.175378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>520</td>\n",
              "      <td>9792</td>\n",
              "      <td>8370</td>\n",
              "      <td>20.085761</td>\n",
              "      <td>14186.13920</td>\n",
              "      <td>58728</td>\n",
              "      <td>68261.38708</td>\n",
              "      <td>15.312451</td>\n",
              "      <td>6.477927</td>\n",
              "      <td>1.368087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>530</td>\n",
              "      <td>10984</td>\n",
              "      <td>9254</td>\n",
              "      <td>20.253199</td>\n",
              "      <td>14487.00838</td>\n",
              "      <td>60000</td>\n",
              "      <td>69041.52340</td>\n",
              "      <td>15.449011</td>\n",
              "      <td>7.162095</td>\n",
              "      <td>1.512578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>540</td>\n",
              "      <td>12333</td>\n",
              "      <td>10127</td>\n",
              "      <td>20.770446</td>\n",
              "      <td>14756.36706</td>\n",
              "      <td>60000</td>\n",
              "      <td>69948.95243</td>\n",
              "      <td>15.271022</td>\n",
              "      <td>7.837750</td>\n",
              "      <td>1.655271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fico_score  loan_total  ...  default_rate  actual_default_rate\n",
              "0         500        3585  ...      2.463470             0.520266\n",
              "1         510        8223  ...      5.565445             1.175378\n",
              "2         520        9792  ...      6.477927             1.368087\n",
              "3         530       10984  ...      7.162095             1.512578\n",
              "4         540       12333  ...      7.837750             1.655271\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "QnJEG4LpnB5g",
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          "height": 308
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        "outputId": "2b6501b0-03e6-4c3d-dfc1-dcfad3937832"
      },
      "source": [
        "loan_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "      <th>default_total</th>\n",
              "      <th>dti</th>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <th>median_annual_inc</th>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>default_rate</th>\n",
              "      <th>actual_default_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>675.000000</td>\n",
              "      <td>16994.527778</td>\n",
              "      <td>3589.111111</td>\n",
              "      <td>18.790417</td>\n",
              "      <td>15088.569697</td>\n",
              "      <td>65678.583333</td>\n",
              "      <td>77391.960108</td>\n",
              "      <td>12.520384</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>0.586645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.356538</td>\n",
              "      <td>11917.752515</td>\n",
              "      <td>3512.765608</td>\n",
              "      <td>1.690671</td>\n",
              "      <td>1633.584599</td>\n",
              "      <td>6846.614297</td>\n",
              "      <td>8038.473538</td>\n",
              "      <td>2.560128</td>\n",
              "      <td>2.718690</td>\n",
              "      <td>0.574166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.676210</td>\n",
              "      <td>12994.231600</td>\n",
              "      <td>55000.000000</td>\n",
              "      <td>63396.102920</td>\n",
              "      <td>7.585562</td>\n",
              "      <td>0.002322</td>\n",
              "      <td>0.000490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>587.500000</td>\n",
              "      <td>10452.750000</td>\n",
              "      <td>214.250000</td>\n",
              "      <td>17.650570</td>\n",
              "      <td>14038.181180</td>\n",
              "      <td>61297.500000</td>\n",
              "      <td>72364.301710</td>\n",
              "      <td>10.453639</td>\n",
              "      <td>0.165818</td>\n",
              "      <td>0.035019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>675.000000</td>\n",
              "      <td>12519.500000</td>\n",
              "      <td>2886.500000</td>\n",
              "      <td>19.083851</td>\n",
              "      <td>14857.024775</td>\n",
              "      <td>63000.000000</td>\n",
              "      <td>74919.065440</td>\n",
              "      <td>13.556549</td>\n",
              "      <td>2.233995</td>\n",
              "      <td>0.471802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>762.500000</td>\n",
              "      <td>23774.500000</td>\n",
              "      <td>6123.750000</td>\n",
              "      <td>20.165171</td>\n",
              "      <td>15353.673887</td>\n",
              "      <td>68250.000000</td>\n",
              "      <td>80706.824483</td>\n",
              "      <td>14.732548</td>\n",
              "      <td>4.739451</td>\n",
              "      <td>1.000935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>850.000000</td>\n",
              "      <td>40444.000000</td>\n",
              "      <td>10164.000000</td>\n",
              "      <td>20.974137</td>\n",
              "      <td>20082.459680</td>\n",
              "      <td>83750.000000</td>\n",
              "      <td>98683.805750</td>\n",
              "      <td>15.449011</td>\n",
              "      <td>7.866386</td>\n",
              "      <td>1.661319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fico_score    loan_total  ...  default_rate  actual_default_rate\n",
              "count   36.000000     36.000000  ...     36.000000            36.000000\n",
              "mean   675.000000  16994.527778  ...      2.777778             0.586645\n",
              "std    105.356538  11917.752515  ...      2.718690             0.574166\n",
              "min    500.000000    124.000000  ...      0.002322             0.000490\n",
              "25%    587.500000  10452.750000  ...      0.165818             0.035019\n",
              "50%    675.000000  12519.500000  ...      2.233995             0.471802\n",
              "75%    762.500000  23774.500000  ...      4.739451             1.000935\n",
              "max    850.000000  40444.000000  ...      7.866386             1.661319\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "u2fUmGA2VM8J",
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          "height": 368
        },
        "outputId": "509b15b0-f764-45f6-9cae-beccfbd9c2e1"
      },
      "source": [
        "loan_df.cov()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "      <th>default_total</th>\n",
              "      <th>dti</th>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <th>median_annual_inc</th>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>default_rate</th>\n",
              "      <th>actual_default_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>11100.000000</td>\n",
              "      <td>8.431757e+04</td>\n",
              "      <td>-3.281917e+05</td>\n",
              "      <td>-164.562715</td>\n",
              "      <td>1.179751e+05</td>\n",
              "      <td>6.540867e+05</td>\n",
              "      <td>7.851304e+05</td>\n",
              "      <td>-261.372425</td>\n",
              "      <td>-254.002627</td>\n",
              "      <td>-53.643365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_total</th>\n",
              "      <td>84317.571429</td>\n",
              "      <td>1.420328e+08</td>\n",
              "      <td>-6.117364e+06</td>\n",
              "      <td>2841.802408</td>\n",
              "      <td>-9.263903e+06</td>\n",
              "      <td>-1.233279e+07</td>\n",
              "      <td>-9.045332e+06</td>\n",
              "      <td>3454.737681</td>\n",
              "      <td>-4734.508923</td>\n",
              "      <td>-999.891190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_total</th>\n",
              "      <td>-328191.714286</td>\n",
              "      <td>-6.117364e+06</td>\n",
              "      <td>1.233952e+07</td>\n",
              "      <td>5018.669412</td>\n",
              "      <td>-2.542762e+06</td>\n",
              "      <td>-1.726291e+07</td>\n",
              "      <td>-2.094780e+07</td>\n",
              "      <td>7646.662485</td>\n",
              "      <td>9550.122450</td>\n",
              "      <td>2016.911035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>-164.562715</td>\n",
              "      <td>2.841802e+03</td>\n",
              "      <td>5.018669e+03</td>\n",
              "      <td>2.858369</td>\n",
              "      <td>-2.164641e+03</td>\n",
              "      <td>-1.040763e+04</td>\n",
              "      <td>-1.219916e+04</td>\n",
              "      <td>4.167275</td>\n",
              "      <td>3.884179</td>\n",
              "      <td>0.820308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <td>117975.085123</td>\n",
              "      <td>-9.263903e+06</td>\n",
              "      <td>-2.542762e+06</td>\n",
              "      <td>-2164.640693</td>\n",
              "      <td>2.668599e+06</td>\n",
              "      <td>9.564547e+06</td>\n",
              "      <td>1.092982e+07</td>\n",
              "      <td>-3359.878600</td>\n",
              "      <td>-1967.960087</td>\n",
              "      <td>-415.617751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_annual_inc</th>\n",
              "      <td>654086.714286</td>\n",
              "      <td>-1.233279e+07</td>\n",
              "      <td>-1.726291e+07</td>\n",
              "      <td>-10407.631551</td>\n",
              "      <td>9.564547e+06</td>\n",
              "      <td>4.687613e+07</td>\n",
              "      <td>5.457827e+07</td>\n",
              "      <td>-16564.670539</td>\n",
              "      <td>-13360.560742</td>\n",
              "      <td>-2821.645746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <td>785130.353840</td>\n",
              "      <td>-9.045332e+06</td>\n",
              "      <td>-2.094780e+07</td>\n",
              "      <td>-12199.161949</td>\n",
              "      <td>1.092982e+07</td>\n",
              "      <td>5.457827e+07</td>\n",
              "      <td>6.461706e+07</td>\n",
              "      <td>-19471.216732</td>\n",
              "      <td>-16212.465719</td>\n",
              "      <td>-3423.945732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>-261.372425</td>\n",
              "      <td>3.454738e+03</td>\n",
              "      <td>7.646662e+03</td>\n",
              "      <td>4.167275</td>\n",
              "      <td>-3.359879e+03</td>\n",
              "      <td>-1.656467e+04</td>\n",
              "      <td>-1.947122e+04</td>\n",
              "      <td>6.554256</td>\n",
              "      <td>5.918103</td>\n",
              "      <td>1.249857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_rate</th>\n",
              "      <td>-254.002627</td>\n",
              "      <td>-4.734509e+03</td>\n",
              "      <td>9.550122e+03</td>\n",
              "      <td>3.884179</td>\n",
              "      <td>-1.967960e+03</td>\n",
              "      <td>-1.336056e+04</td>\n",
              "      <td>-1.621247e+04</td>\n",
              "      <td>5.918103</td>\n",
              "      <td>7.391278</td>\n",
              "      <td>1.560980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_default_rate</th>\n",
              "      <td>-53.643365</td>\n",
              "      <td>-9.998912e+02</td>\n",
              "      <td>2.016911e+03</td>\n",
              "      <td>0.820308</td>\n",
              "      <td>-4.156178e+02</td>\n",
              "      <td>-2.821646e+03</td>\n",
              "      <td>-3.423946e+03</td>\n",
              "      <td>1.249857</td>\n",
              "      <td>1.560980</td>\n",
              "      <td>0.329667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        fico_score  ...  actual_default_rate\n",
              "fico_score            11100.000000  ...           -53.643365\n",
              "loan_total            84317.571429  ...          -999.891190\n",
              "default_total       -328191.714286  ...          2016.911035\n",
              "dti                    -164.562715  ...             0.820308\n",
              "avg_loan_amnt        117975.085123  ...          -415.617751\n",
              "median_annual_inc    654086.714286  ...         -2821.645746\n",
              "avg_annual_inc       785130.353840  ...         -3423.945732\n",
              "int_rate               -261.372425  ...             1.249857\n",
              "default_rate           -254.002627  ...             1.560980\n",
              "actual_default_rate     -53.643365  ...             0.329667\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVpwoIfaowLt",
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          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "06f36eda-36b8-47a3-fc05-0d52ce0ad937"
      },
      "source": [
        "loan_df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "      <th>default_total</th>\n",
              "      <th>dti</th>\n",
              "      <th>avg_loan_amnt</th>\n",
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              "      <th>fico_score</th>\n",
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              "      <td>-0.146124</td>\n",
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              "      <th>default_total</th>\n",
              "      <td>-0.886782</td>\n",
              "      <td>-0.146124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.845046</td>\n",
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              "      <td>-0.717776</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>-0.923870</td>\n",
              "      <td>0.141039</td>\n",
              "      <td>0.845046</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.897630</td>\n",
              "      <td>0.962789</td>\n",
              "      <td>0.845046</td>\n",
              "      <td>0.845046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <td>0.685468</td>\n",
              "      <td>-0.475837</td>\n",
              "      <td>-0.443113</td>\n",
              "      <td>-0.783764</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855159</td>\n",
              "      <td>0.832335</td>\n",
              "      <td>-0.803379</td>\n",
              "      <td>-0.443113</td>\n",
              "      <td>-0.443113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_annual_inc</th>\n",
              "      <td>0.906772</td>\n",
              "      <td>-0.151144</td>\n",
              "      <td>-0.717776</td>\n",
              "      <td>-0.899118</td>\n",
              "      <td>0.855159</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991677</td>\n",
              "      <td>-0.945029</td>\n",
              "      <td>-0.717776</td>\n",
              "      <td>-0.717776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <td>0.927058</td>\n",
              "      <td>-0.094418</td>\n",
              "      <td>-0.741849</td>\n",
              "      <td>-0.897630</td>\n",
              "      <td>0.832335</td>\n",
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              "      <td>-0.741849</td>\n",
              "      <td>-0.741849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>-0.969029</td>\n",
              "      <td>0.113229</td>\n",
              "      <td>0.850278</td>\n",
              "      <td>0.962789</td>\n",
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              "      <td>-0.945029</td>\n",
              "      <td>-0.946145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850278</td>\n",
              "      <td>0.850278</td>\n",
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              "    <tr>\n",
              "      <th>default_rate</th>\n",
              "      <td>-0.886782</td>\n",
              "      <td>-0.146124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.845046</td>\n",
              "      <td>-0.443113</td>\n",
              "      <td>-0.717776</td>\n",
              "      <td>-0.741849</td>\n",
              "      <td>0.850278</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_default_rate</th>\n",
              "      <td>-0.886782</td>\n",
              "      <td>-0.146124</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.845046</td>\n",
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              "      <td>1.000000</td>\n",
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            ],
            "text/plain": [
              "                     fico_score  loan_total  ...  default_rate  actual_default_rate\n",
              "fico_score             1.000000    0.067153  ...     -0.886782            -0.886782\n",
              "loan_total             0.067153    1.000000  ...     -0.146124            -0.146124\n",
              "default_total         -0.886782   -0.146124  ...      1.000000             1.000000\n",
              "dti                   -0.923870    0.141039  ...      0.845046             0.845046\n",
              "avg_loan_amnt          0.685468   -0.475837  ...     -0.443113            -0.443113\n",
              "median_annual_inc      0.906772   -0.151144  ...     -0.717776            -0.717776\n",
              "avg_annual_inc         0.927058   -0.094418  ...     -0.741849            -0.741849\n",
              "int_rate              -0.969029    0.113229  ...      0.850278             0.850278\n",
              "default_rate          -0.886782   -0.146124  ...      1.000000             1.000000\n",
              "actual_default_rate   -0.886782   -0.146124  ...      1.000000             1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js-n5YxPhYG3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "f46a0f64-caa1-48a5-9f4e-7e5953ee14b2"
      },
      "source": [
        "loan_df.corr(method='spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "      <th>default_total</th>\n",
              "      <th>dti</th>\n",
              "      <th>avg_loan_amnt</th>\n",
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              "      <th>fico_score</th>\n",
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              "    <tr>\n",
              "      <th>loan_total</th>\n",
              "      <td>0.043243</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>default_total</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>-0.936422</td>\n",
              "      <td>0.029344</td>\n",
              "      <td>0.967310</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.576577</td>\n",
              "      <td>-0.877666</td>\n",
              "      <td>-0.890862</td>\n",
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              "      <td>0.967310</td>\n",
              "      <td>0.967310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_loan_amnt</th>\n",
              "      <td>0.661261</td>\n",
              "      <td>-0.344916</td>\n",
              "      <td>-0.601544</td>\n",
              "      <td>-0.576577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.750476</td>\n",
              "      <td>0.711969</td>\n",
              "      <td>-0.656371</td>\n",
              "      <td>-0.601544</td>\n",
              "      <td>-0.601544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_annual_inc</th>\n",
              "      <td>0.956772</td>\n",
              "      <td>0.035934</td>\n",
              "      <td>-0.889429</td>\n",
              "      <td>-0.877666</td>\n",
              "      <td>0.750476</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.979263</td>\n",
              "      <td>-0.955868</td>\n",
              "      <td>-0.889429</td>\n",
              "      <td>-0.889429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <td>0.966281</td>\n",
              "      <td>0.048649</td>\n",
              "      <td>-0.899871</td>\n",
              "      <td>-0.890862</td>\n",
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              "      <td>-0.966795</td>\n",
              "      <td>-0.899871</td>\n",
              "      <td>-0.899871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>-0.997169</td>\n",
              "      <td>-0.035006</td>\n",
              "      <td>0.950322</td>\n",
              "      <td>0.937709</td>\n",
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              "      <td>0.950322</td>\n",
              "      <td>0.950322</td>\n",
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              "    <tr>\n",
              "      <th>default_rate</th>\n",
              "      <td>-0.939511</td>\n",
              "      <td>0.077992</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.967310</td>\n",
              "      <td>-0.601544</td>\n",
              "      <td>-0.889429</td>\n",
              "      <td>-0.899871</td>\n",
              "      <td>0.950322</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_default_rate</th>\n",
              "      <td>-0.939511</td>\n",
              "      <td>0.077992</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.967310</td>\n",
              "      <td>-0.601544</td>\n",
              "      <td>-0.889429</td>\n",
              "      <td>-0.899871</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     fico_score  loan_total  ...  default_rate  actual_default_rate\n",
              "fico_score             1.000000    0.043243  ...     -0.939511            -0.939511\n",
              "loan_total             0.043243    1.000000  ...      0.077992             0.077992\n",
              "default_total         -0.939511    0.077992  ...      1.000000             1.000000\n",
              "dti                   -0.936422    0.029344  ...      0.967310             0.967310\n",
              "avg_loan_amnt          0.661261   -0.344916  ...     -0.601544            -0.601544\n",
              "median_annual_inc      0.956772    0.035934  ...     -0.889429            -0.889429\n",
              "avg_annual_inc         0.966281    0.048649  ...     -0.899871            -0.899871\n",
              "int_rate              -0.997169   -0.035006  ...      0.950322             0.950322\n",
              "default_rate          -0.939511    0.077992  ...      1.000000             1.000000\n",
              "actual_default_rate   -0.939511    0.077992  ...      1.000000             1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2Gj7EF7mENs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "15e720b1-5c15-4701-a5fe-7dbca7a269ca"
      },
      "source": [
        "sns.distplot(loan_df['median_annual_inc'], hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb439757ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rg0VjtQJnjwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "2f509d9a-eac6-400a-f8e4-f7b7de2586ec"
      },
      "source": [
        "loan_df[['fico_score','median_annual_inc']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>median_annual_inc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.956772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_annual_inc</th>\n",
              "      <td>0.956772</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   fico_score  median_annual_inc\n",
              "fico_score           1.000000           0.956772\n",
              "median_annual_inc    0.956772           1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-V3UdKlmELF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "91e28d46-3981-47a0-ad52-bf9ebf2acf37"
      },
      "source": [
        "sns.regplot(x='fico_score',y='median_annual_inc',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb43962b9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leVXkVGwrywm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "8d53aa46-8d41-4a99-8b6b-7e76e328bfe3"
      },
      "source": [
        "loan_df[['fico_score','avg_annual_inc']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>avg_annual_inc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.966281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_annual_inc</th>\n",
              "      <td>0.966281</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                fico_score  avg_annual_inc\n",
              "fico_score        1.000000        0.966281\n",
              "avg_annual_inc    0.966281        1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfWkXAKjmECu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "c1612170-74cd-4b4d-d2f6-04d7ebc1199f"
      },
      "source": [
        "sns.regplot(x='fico_score',y='avg_annual_inc',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4369653c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU0e4p1vsMry",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "3ab0cf20-934d-4091-9bc7-1cd2f7c14513"
      },
      "source": [
        "loan_df[['fico_score','dti']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>dti</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.936422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>-0.936422</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            fico_score       dti\n",
              "fico_score    1.000000 -0.936422\n",
              "dti          -0.936422  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oef0ixLqmD-E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "494132a8-7a51-4528-cea7-579564f08ea7"
      },
      "source": [
        "sns.regplot(x='fico_score',y='dti',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4368e5a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6huCxX88_5h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "b09b6674-6f62-489f-96ae-d222669ef11c"
      },
      "source": [
        "loan_df[['fico_score','int_rate']].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>int_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.969029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>-0.969029</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            fico_score  int_rate\n",
              "fico_score    1.000000 -0.969029\n",
              "int_rate     -0.969029  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uuYZuHJ8_q8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "c147e9d0-73b6-47f2-ef57-560ec1996856"
      },
      "source": [
        "sns.regplot(x='fico_score',y='int_rate',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4368d3278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RorQl2qQ9Q_E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "63fd55d7-7cbd-4458-d07c-1f386858080b"
      },
      "source": [
        "sns.distplot(loan_df['int_rate'], hist=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb43683f4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUjd_MDxbvig",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "605d4c56-d33f-4286-8206-1356d61494d2"
      },
      "source": [
        "import scipy.stats as stats\n",
        "stats.probplot(loan_df['int_rate'], dist=\"norm\", plot=plt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((array([-2.07334886, -1.68217954, -1.44830429, -1.27437681, -1.13225357,\n",
              "         -1.00993636, -0.90112151, -0.80205412, -0.71029762, -0.62416759,\n",
              "         -0.54243995, -0.46418751, -0.38868208, -0.31533241, -0.24364313,\n",
              "         -0.17318609, -0.10357945, -0.03447167,  0.03447167,  0.10357945,\n",
              "          0.17318609,  0.24364313,  0.31533241,  0.38868208,  0.46418751,\n",
              "          0.54243995,  0.62416759,  0.71029762,  0.80205412,  0.90112151,\n",
              "          1.00993636,  1.13225357,  1.27437681,  1.44830429,  1.68217954,\n",
              "          2.07334886]),\n",
              "  array([ 7.58556196,  7.96854839,  8.00050755,  8.40436779,  8.81612608,\n",
              "          9.17925418,  9.54207879,  9.94439109, 10.17342593, 10.54704384,\n",
              "         10.83639354, 11.07060021, 11.40585966, 11.84989813, 12.19602541,\n",
              "         12.64158738, 12.99737662, 13.36212073, 13.75097687, 13.84232878,\n",
              "         13.93910593, 13.99130432, 14.13094166, 14.38872081, 14.45885661,\n",
              "         14.56445333, 14.71287103, 14.79158065, 14.83095961, 14.93735761,\n",
              "         15.04821175, 15.27102165, 15.31245098, 15.35228452, 15.4402116 ,\n",
              "         15.44901129])),\n",
              " (2.5196349375983393, 12.520383785694445, 0.9516744621680466))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GoIDXBN5slea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "957d3bff-dd4f-47f0-8814-6ae633e08bea"
      },
      "source": [
        "loan_df[['fico_score','loan_total']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>loan_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.043243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_total</th>\n",
              "      <td>0.043243</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            fico_score  loan_total\n",
              "fico_score    1.000000    0.043243\n",
              "loan_total    0.043243    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBkMQ5RzVCLj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "877ae722-d9c9-4db7-b6ca-ca9703228a0e"
      },
      "source": [
        "sns.regplot(x='fico_score',y='loan_total',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb436723ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWBM9J5UoFKv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "36fe896f-f262-482d-fc85-4db319331c50"
      },
      "source": [
        "#@title\n",
        "coeff = np.poly1d(np.polyfit(loan_df['fico_score'], loan_df['loan_total'], 5))\n",
        "\n",
        "t = loan_df['fico_score']\n",
        "plt.plot(loan_df['fico_score'], loan_df['loan_total'], 'o', t, coeff(t), '-')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rJTBgAv27pNK/m9sBxYjOo2DlRFgzxU5mp1yhdwZKAZzKggW/h0Yd4fLb3Y4mtjRIgZbXwkrtZuomTQZKASz5K5w4CDc8C3Hxpe+vQqvLXfD9Htj6vtuRxCxNBkrtXm2rKTqPgsapbkcTm1r3tQvgrNCGZLdoMlCxLT8P5jwINRra+YdUucxam0n38UtoMWYu3ccvCXzUdlw8XH6nnSZ87/rwBqmKpMlAxbaVr8G+DTBgPFSt5XY0Ua1gGo/MrFMYIDPrFGNnbgw8IaSOgIRq2s3UJZoMVOw6tse2FVzYB9oNdjuaqDdhQTqncvPPKivTNB6JSdBhGGz8L5w4HIYIVUk0GajYNf9h8OXBwAm6TkEIFDddR5mm8eh8F+Rn226mKqI0GajY9OVCO0Ha1aN1nYIQKW66jjJN43HeRdDiGlg5ybbnqIjRZKBiT84JmPtbqJ8C3X7pdjQVxuh+KSQmnN0tN6hpPLrcDccyYducEEanSqPJQMWej8fD0W/tmIJKlUvfXwVkcGoy44a0L/80Hm36QVIz7WYaYTodhYotezfAFy9Ap1vhAp1vItQGpyaXfw6nuHg75mPhH2HfRji/fWiCUyXSOwMVO3z5dkxBtbrQ+zG3o1ElSR0B8VVgzRtuRxIzNBmo2LFyEmSuhn7jbEJQ3pVYB9reABumQ+5pt6OJCZoMVGw4tgcWPw6tekL7oW5HowKROgJOZ0H6XLcjiQmaDFRsmP878OXCdU/pmIJo0eIaqN0U1vzb7UhigiYDVfFtm2dnw7zmYajb0u1oVKDi4qDjzbDjY8j61u1oKjxNBqpiyz4O80bDee2g2wNuR6PKquPPAAPr3nI7kgpPk4Gq2D76OxzbDdc/C/EJbkejyqrOBba6aN2b4PO5HU2FVmoyEJGmIvKRiGwRkc0i8iunvK6ILBKR7c7POk65iMhzIpIhIhtEpJPfsUY6+28XkZF+5ZeJyEbnPc+JaKWuCoE962D5S5D2C2jWxe1oVLBSb7HVRF8vdTuSCi2QO4M84LfGmHZAV+A+EWkHjAEWG2NaA4ud1wADgNbOYxTwEtjkATwKdAE6A48WJBBnnzv93te//L+aimn5efD+r6B6A+j1qNvRqPJoez1UqQ1r33Q7kgqt1GRgjNlrjFnjPP8e2AokA4OA153dXgcK5gAeBLxhrGVAkog0AvoBi4wx3xljjgCLgP7OtlrGmGXGGAO84XcspYKz8jXYuw76j7dTIyvPKPMCOAmJcOmP7cSCp7IiE2QMKlObgYg0B1KB5UBDY8xeZ9M+oKHzPBnY5fe23U5ZSeW7iygv6vNHicgqEVl18ODBsoSuYknWrh/WKbj4JrejUX6CXgAndQTknYZN70QkzlgUcDIQkRrAO8CDxphj/tucb/QmxLGdwxjzqjEmzRiT1qBBg3B/nIpGxsDsB+xPHVPgOUEvgNOoIzS8RKuKwiigZCAiCdhEMNUYM9Mp3u9U8eD8POCUZwJN/d7exCkrqbxJEeVKld3qKbDjI+j7F9sTRXlK0AvgiNi7gz1rYP/mMESmAulNJMAkYKsx5mm/TbOBgh5BI4H3/MpvdXoVdQWOOtVJC4C+IlLHaTjuCyxwth0Tka7OZ93qdyylAnfkGzvTZcsetgeR8pxyLYDT/icQlwBrp4Y4KgWB3Rl0B24BeorIOucxEBgP9BGR7UBv5zXAPGAHkAG8BtwLYIz5DvgLsNJ5PO6U4ewz0XnPV8D8EPxuKpb4fDD7fkDgxue1esijyrUATvV6cNFA2DAN8nLCFGHsKnU9A2PMp0Bx/7N6FbG/Ae4r5liTgclFlK8CLiktFqWKtWoS7Pwf3PAPuzCK8qSCtQ4mLEhnT9YpGiclMrpfSuBrIKTeAlvegy/nQ7tBYYw09ujiNir6fbcTFj1qZyTtNLL0/ZWryrUATqueULOxbUjWZBBSOh2Fim4+H7x3v10dS6uHKr64eOg4HDI+tNOSq5DRZKCi28rX4JtPod/foXaT0vdX0a/jzWB8sF4nrwslTQYqeh3+ylYPXdjHdjtUsaFeK7igu60qMmEf3hQztM1ARSefD967D+Irw43Plal6aNbazOAbMFVElPpvlDoCZt0D334BF3RzL9AKRO8MVHRa/rK9EAwYD7UaB/y2oKdDUBET0L9Ru0FQuYaOSA4hTQYq+hzKgMWPQZv+0GF4md4a9HQIKmIC+jeqXB0uGQKb34Xs7yMcYcWkyUBFl/xcmHU3VKpqF6wpY++hoKdDUBET8L9Rx5sh96Rd0lSVmyYDFV0WPwa7V9pJ6Go1KvPbyzUdgoqIgP+NmnaBOs21V1GIaDJQ0WPbXPj8eUi7HdoPDeoQ5ZoOQUVEwP9GIraacOdSO225KhdNBio6fLcT3r3HTmXcf1zQhxmcmsy4Ie1JTkpEgOSkRMYNaa+9iTykTP9Gl/4UMLBxeqTDrHDERGk/3bS0NLNq1Sq3w1CRkHsaJveFI1/DXf+zVQNKFZjcH04ehvtW6Aj0UojIamNMWlHb9M5Aed+C38Pe9TD4ZU0E6lwdhsGhL2HPWrcjiWo66Ex524b/2hlJu/3STl+sFGcPSmtTux7z4ioTv34aJHdyO7SopXcGyrsOpsP7v4JmV0CvR9yORnlE4UFp6UfjWJh3Gdnrpus6B+WgyUB5U84JmH4rJCTC0MkQn+B2RMojihqUNj3vSqrkHLGzmaqgaDJQ3mMMzPmNvTP40cQyTTehKr6iBqUt9bXnkKmlYw7KQZOB8p41r9ulDXuMgVbXuh2N8piiBqXlUYnFla6BLz+Ak98V8S5VGk0Gylv2rod5v4OW18LVo92ORnlQcYPSzrtyJOTn2PmKVJlpMlDeceRr+M9PoVo9Wz0UF1/qW1TsKW5Q2rU9ekODtrB+mtshRiXtWqq84fv98MZgyD0FP58P1esHfShdr6DiK3Yd5Q7D4MNH7cJH9VpFPrAopncGyn2nsuDNIXB8P9w8Axq2C/pQul5BjLv0J4Do3UEQNBkod+WcgP/8xPYcGjYVml5ersPpegUxrlZjaNnDdkDw+dyOJqpoMlDuycuxYwl2r7RtBK16lvuQul6BosNwyPoWdi1zO5KooslAucOXD+/eZQcJXf8sXDw4JIfV9QoUba+HhOo65qCMtAHZoyp0I6gxMO8h2DwTej8Gl40M2aFH90th7MyNZ1UV6XoFMaZydWg3iNwNM+m7eSBfH/VVvP9DYaDJwIMKGkELLmgFjaDAWX/MUZswlvwVVk2G7g/ClQ+G9NAFv39UnhcVMp9W782Vef/h4pOfsZMriv0/pH6gycCDSmoELfhDjtqE8fnzsPRJ6DQSev85LB9RbLdDFTPGrK7N26YeQ+KXMsd3BXDu/yF1Nm0zcMmstZl0H7+EFmPm0n38krO6PgbSCBpIrxlPdbP0+eCT/4OFf4R2g+H6Z3QhEhU2mUezmZXfnavjNtCArDPl2pGgeJoMXFDaRTqQRtBQJYyIyP4ept8CH/0N2v8Ehryqo4tVWDVOSmRm/lVUEh83xn9+VrkqmiYDF5R2kQ5kQfBQJYywO5QBr/WC9PnQb5xNBJWqBH24ku6olCowul8Keyo1Y52vJT+KXwpoR4LSaJuBC0q7SAfSCBpIr5nGSYlkFvFZ/gkjrG0K6R/AzDvtWgS3zoIWV5frcIG2kyhV8Pfw4dzePJT3Kj1q7WXwgAH6d1ICTQYuCOQiXVojaCgSRtgurj6fbST+6O9wfns7sjipWfDHcwTSsK5UgcGpyZAyFp6awpSOX0LqHW6H5GmaDMLhyNew7GU4usvWjUu8/RlXCSSeqQ1Ps+zkUU764tnga8lnvos5nlC/zLew5U0YYbm4Zn8P794N2+bApT+FG/5hVysLAU9Ue6noUq0uXHQdbJwOff9SrirKik6TQSgdTIdPn8G3YTp5Rtjha0SVeKhfvRI1Kwv48sDno7nJ5/zEHHzZJ/gFHwBwrOaF1NrXB9J7QPPuUKVmSEIqKWGE/OJ6KAOm/QwOZ9j2ga73lKnHUGlVVoHcUSl1jo4j7BoH6fNDNtK9ItJkEAp71sHSp2Dr++TFV+XN/P68lDOA/dQFIDEvnnFD2p91YasKtjpl/0bY8TG1dnwMq/8Fy1+ydxDJadDyGjtfT5PLw9L7JtCLa6ntCscPwufPwcqJ9i6giPaB0o4RSJWVji5WQWl1LdRsDOumajIogRhj3I4hKGlpaWbVqlXuBvHNF7ZuPONDqFIbOt/JwBXt2XL03MXbk5MS+WxMKROx5Z6G3Stgx8f2sWctGB/UaAhtb4C2N8IF3SE+NDm88AUY7MXVP3GVuE+bKjYJrHgNk3uaBXFX8ZeTP4Kkpmdd7AP5nO7jlxSZmAqfN88NolPR4cPH4LNn4Tdboeb5bkfjGhFZbYxJK2qb3hkEY/9mmDcavvnMrsrV6xG4/A6oWputi+YW+ZaAql4Sqtpv1C2utsc8dQQyFsOW92DtVPvNu1o9WwfabhC0uMb21AlSII3QRbUrJOYe4ficP0DcAsg9xa4m13HXNz3Zctr5T1boW30gbROBVlnp6GIVlI43w6dP23UO/KZA0S8XP9BkUFbHD8CbQ8GXa+vFLxtpJ8ZyhLReO7EOtB9qHzkn7B3Ilvdg00xY8wZUrQ0p10GbfnBBN6hxXpk/orSLq//FuA7HGFVpLrfGLyQxLwcuHQpX/45hk3aTmXv27+x/sQ/kQq/tASqs6l8ITbvaqqLuvwIR7apcSKmDzkRksogcEJFNfmV1RWSRiGx3ftZxykVEnhORDBHZICKd/N4z0tl/u4iM9Cu/TEQ2Ou95TsTDcxTk58J/f26/sd/yLlxx71mJAAIbMBYUZyZGhk6G0V/BsLcgZSBsmwv/HQlPtobn02D2A/bbT9a35fs8gLwcetbKZET8Ip5MeJlPq/yKu+Ln8KHvMkZU+Yddg6BBm1Iv9oEMkAvbeVOqQMefwaEvYbetXvbMCH2PCOTOYArwT+ANv7IxwGJjzHgRGeO8fhgYALR2Hl2Al4AuIlIXeBRIAwywWkRmG2OOOPvcCSwH5gH9gfnl/9XCYNEj8M2nPJbwIFOe/ZbGSQfPua2MyKyZCVXhooH2kZ9rG7C//Ry++dzeOaxx/qlqNbF3DM262hWgKteAKjWgck3nZ3U773tcnG3MPrwdMtfAnjWQuRr2bWRSfg4kwGFTk4W+NF7IG0RmpQsYN6D9mXBK+1YfSMOvzjaqwu7im2D+w7DuTWh6uXZVLiSgBmQRaQ7MMcZc4rxOB3oYY/aKSCPgY2NMioi84jx/y3+/gocx5i6n/BXgY+fxkTHmIqd8uP9+JYl4A/KG/8LMO3jDN4BHcm45U1y4IdR1Ph8c2GITQ0GCOL6/hDeITQrGQO4JW1S5BjTqCMmpkHwZC7OSeex/37Pn6OmAegJB0Q3ReqFXrpt5F6TPg9+m0/3pZQF1WqhIwtGA3NAYs9d5vg9o6DxPBnb57bfbKSupfHcR5UUSkVHAKIBmzco/ojVg+zbB7AdYJ+14PGf4WZs8NwI2Lg7Ov8Q+uoyyF/msb+HkIcg+DjnHbftD9vf2ebbz2vjsaOHkTlC/zVldWfsCfbsX/5GBfKvXhl/lCak32/WRt81hdL/u2lXZT7kbkI0xRkQi0j/VGPMq8CrYO4NIfCanjsDbN0NiEncevJ+8Ik6Zp28rRaDOBfYRRnqxV1Hhgivt1CjrpjL41p8AWjVZINhksF9EGvlVEx1wyjOBpn77NXHKMrFVRf7lHzvlTYrY3xt8PnjnTjiaCT+fR+Wpx0F7vCgVveLibDfTj8dD1i4GpzaN2Yt/YcFOYT0bKOgRNBJ4z6/8VqdXUVfgqFOdtADoKyJ1nJ5HfYEFzrZjItLV6UV0q9+x3PfJeMhYBAOegKadtceLUhVBh+GAgfVvuR2JpwTStfQt4AsgRUR2i8jtwHigj4hsB3o7r8H2BtoBZACvAfcCGGO+A/4CrHQejztlOPtMdN7zFV7pSZQ+Hz55ws5rkvYLwFaFjBvSnuSkRATb0OSpxmOlVOnqXGAHdq6bau/+FaDTUZyloMdL5aM7eL/Kn8hNakmd+xbbrpxKqYpj/TR49y64bS40v9LtaCKmpN5EutKZo6B75HdZR3g54RmyTTw/OnwPsyc1fI8AAAzQSURBVDYddjs0pVSotb3RjrdZO9XtSDxDk4GjYDTiT+M/JiVuNw/m3seO3DoxOxpRqQqtcjW45CbYMst2s1aaDArY7qGGW+IXsdZ3IUt9l/qVK6UqnI4jIPekHbVfjFhac1uTgaNxUiLd4jbTKm4vb+T1OatcKVUBNe0M9VoXW1VUUHWcmXUKww8T2VXUhKDJwDG6Xwq3JXzId6YG83xdAO02qlSFJmInr/v2czj81TmbY20iO00GjsEtoXfcauZV6kMOlbXbqFKxoMNwu0b56innbIq1iex0PYMCq6cQZ3yMuP/PjKjT3O1olFKRUKsRtL3ezvTbY0z41iaJAnpnAJCXA2teh9Z9QROBUrGlyz1wOgs2vH1WcazNOKDJAGDb+3aa5853uh2JUirSmnWFRh1g+St2ll9HrM04oNVEACsnQdIF0KqX25EopSJNxN4dzLobdnwErX5YyyCWZuPVO4P9m+3C9pffbmc0VErFnkuGQPUGsOxltyNxjV79Vk6C+CqQekvp+yqlKqZKVeyElNsXFNnNNBbEdjI4fcw2Gl3yI6hW1+1olFJuSrsd4hJs20EMiu1ksOFtu/Rj5zvcjkQp5baaDW110bqpcPqo29FEXOwmA2NgxWvQ2C76rpRSdLnbfkFc9x+3I4m42E0GX38Kh9Lhcr0rUEo5kjtB0y62qsiXX/r+kZa5GjIWh2VRnthNBitfg6pJtr1AKaUKdLkbjuyE7QsD2j2iM5v+7yl4924woU9UsZkMju2FrXMgdQQkVMyh5UqpILW9AWo2hmUvlbprRGc2PX7Q9na69CcQnxDyw8dmMljzus2sl9/udiRKKa+JT7CdSnZ+Avu3lLhrRGc23TgdfHn2S2wYxF4yyM+FVf+CC3tD3ZZuR6OU8qLLfg6VqsLykgehRWxmU2PsuguNO8F5bUN7bEfsJYNtc+H4Pm04VkoVr1pdWx2z4W04+V2xuxU3g2nIZzbdux4ObLbrL4RJTCWDWWszWfPOBHab+lw1s1KFXbFIKRUCXe6GvNNFrnVQIGIzm66bCvGVof3Q0B7XT8wkg1lrM/nLzBUk5R1ial5vdh3NqdBL2CmlyqnhxdDialg50VYvFyEiM5vmZcPG/8JF10FindAdt5CYmbV0woJ0DudWphdPkoBt8Clo6ImVWQmVUmXU5R6YNhy2zYGLbypyl7DPbJo+H04dgY7haTguEDN3BgUNOoY4ckg4p1wppc7Rpp+d3t7N2UzX/QdqNoJW14b1Y2ImGUSsoUcpVXHExdu2g13L4KuPIv/53++DjEXQYZiNJYxiJhnE2hJ2SqkQSfuFXQ53/u/sErmRtOFtMD7oeHPYPypmkkGsLWGnlAqRhKrQ/wk49GWp4w6KE9SUFQVjC5p0hvqtg/rcsoiZBmSIrSXslFIhlNIf2vSHT56A9j+GWo0CfmvBlBUFI5ULpqwASr4eZa6xk2ne8I9yhR6omLkzUEqpcuk/3nYxXfjHMr0t6Ckr1r0JlRKL7cUUapoMlFIqEHVbwJUPwqYZsHNpwG8LasqK3FOw8R07aV7V2mWNNCiaDJRSKlBX/hqSmsG80cUORCssqJ6M2+ZC9tGwTj9RmCYDpZQKVEKirS46uBVWvBrQW4LqybhuKtRuCi2uKU+0ZaLJQCmlyiJlIFzYBz4aZ8cBlKLMPRmPZtoxDR2GQ1zkLtEx1ZtIKaXKTQQGPAEvdoVFj8CQ0u8QytSTcf1bgIGOw8sXZxlpMlBKqbKq1wq6/RKWPgmX3QYXdCvX4WatzWTCgnT2ZJ3kk8SJVKuXRv0Ir7ei1URKKRWMq35r6/XnPgT5eUEfxn/pzE7yJc3MXp4+mBbxGZU1GSilVDAqV4N+f7eLzqycGPRh/MchDI3/HydMFWbldA7P0pkl8EwyEJH+IpIuIhkiMsbteJRSqlRtb4BWPeGjv8HxA0EdomC8QVWyuT5+GfPyu3CSqueMQwhqSosy8EQyEJF44AVgANAOGC4i7dyNSimlSiECAybYQWLvPwjZx8t8iMZJiZzPYV6v/AQ15RRv5/c4U17AvyrJ8MOUFqFMCJ5IBkBnIMMYs8MYkwNMAwa5HJNSSpWu/oXQ+8+QPg9e7g7ffF6mtz/ZcR/zq/yeS2Qnv8q5l1XmonPGIQQ9pUUZeCUZJAO7/F7vdsqUUsr7ut0Pt821M43+ayB88Ht7t1CS/FxY+CeuWHYPcbUbc3uVp5jtu7LIcQhBTWlRRlHVtVRERgGjAJo1a+ZyNEop5ad5d7jnczv2YNkLsH0h3PQyNEk7d98j38A7t8PulXD5HdTu+zemJVQt9tCNkxLJLOLCH8rFubxyZ5AJNPV73cQpO4sx5lVjTJoxJq1BgwYRC04ppQJSpQZc/zTc8q69M5jUBz58zC5qX2DrHHjlKjiYDj+eAtc9ZddMKEEkFufyyp3BSqC1iLTAJoFhQORmaFJKqVBq1RPu/dxWF336NHy5wK5LsGmGXSCncSoM/ZedCTUABVVGdmDaKRonJTK6X0pI12cRY0zIDlYeIjIQeBaIByYbY/5W0v5paWlm1apVEYlNKaWClv4BvP9LOL7fvu56r21wrlQl4qGIyGpjTBH1Vt65M8AYMw+Y53YcSikVUin9oekyWPoUNL8SUga4HVGRPJMMlFKqwqpWF/qVWNnhOq80ICullHKRJgOllFKaDJRSSmkyUEophSYDpZRSaDJQSimFJgOllFJoMlBKKYWHpqMoKxE5CHwT5NvrA4dCGE44RVOsEF3xRlOsEF3xRlOsEF3xlifWC4wxRc7yGbXJoDxEZFVx83N4TTTFCtEVbzTFCtEVbzTFCtEVb7hi1WoipZRSmgyUUkrFbjJ41e0AyiCaYoXoijeaYoXoijeaYoXoijcsscZkm4FSSqmzxeqdgVJKKT+aDJRSSlXMZCAiX4vIRhFZJyKrnLK6IrJIRLY7P+s45SIiz4lIhohsEJFOHon3zyKS6ZStc5YFLdh/rBNvuoj0i3CsSSIyQ0S2ichWEbnCq+e2mFi9el5T/GJaJyLHRORBD5/b4uL16vn9tYhsFpFNIvKWiFQVkRYistyJ6W0RqezsW8V5neFsb+6BWKeIyE6/89rR2Td0fwfGmAr3AL4G6hcq+z9gjPN8DPCE83wgMB8QoCuw3CPx/hl4qIh92wHrgSpAC+ArID6Csb4O3OE8rwwkefXcFhOrJ89roVjigX3ABV49tyXE67nzCyQDO4FE5/V04Dbn5zCn7GXgHuf5vcDLzvNhwNsRPJfFxToFGFrE/iH7O6iQdwbFGIS9OOD8HOxX/oaxlgFJItLIjQADNAiYZozJNsbsBDKAzpH4YBGpDVwNTAIwxuQYY7Lw4LktIdbiuHZei9AL+MoY8w0ePLdF8I+3OG6f30pAoohUAqoBe4GewAxne+FzW3DOZwC9RERcjHVPCfuG7O+goiYDAywUkdUiMsopa2iM2es83wc0dJ4nA7v83rvbKYukouIFuN+59ZtcUD2Au/G2AA4C/xKRtSIyUUSq481zW1ys4L3zWtgw4C3nuRfPbWH+8YLHzq8xJhN4EvgWmwSOAquBLGNMXhHxnInV2X4UqOdWrMaYhc7mvznn9RkRqVI4VkfQ57WiJoMrjTGdgAHAfSJytf9GY++vvNSntqh4XwJaAR2xfxRPuRhfgUpAJ+AlY0wqcAJbdXGGh85tcbF68bye4dRb3wj8t/A2D53bM4qI13Pn10lIg7BfEBoD1YH+rgZVjKJiFZERwFjgIuByoC7wcKg/u0ImAye7Yow5ALyLvR3dX3D75Pw84OyeCTT1e3sTpyxiiorXGLPfGJNvjPEBr/HDLbWb8e4GdhtjljuvZ2AvuF48t0XG6tHz6m8AsMYYs9957cVz6++seD16fnsDO40xB40xucBMoDu2SqVSEfGcidXZXhs47GKs3Ywxe52qoGzgX4ThvFa4ZCAi1UWkZsFzoC+wCZgNjHR2Gwm85zyfDdzqtMp3xd6W7SVCiou3UL3fTdjfoSDeYU6PhxZAa2BFJGI1xuwDdolIilPUC9iCB89tcbF68bwWMpyzq1w8d24LOStej57fb4GuIlLNqfsv+Lv9CBjq7FP43Bac86HAEueuzK1Yt/p9IRBs24b/eQ3N30G4WsXdegAtsb0W1gObgT845fWAxcB24EOgrlMuwAvY3g0bgTSPxPtvJ54Nzj94I7/3/MGJNx0YEOF4OwKrnLhmAXU8fG6LitWT59X5/OrYb6C1/co8eW5LiNeT5xd4DNiGvYj+G9urqSU2IWVgq7mqOPtWdV5nONtbeiDWJc553QS8CdQI9d+BTkehlFKq4lUTKaWUKjtNBkoppTQZKKWU0mSglFIKTQZKKaXQZKCUUgpNBkoppYD/Bz5Al2ClJ/fAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPVWi6O5sv9v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "03b3c517-07c4-4f05-b880-dc1fad924dfa"
      },
      "source": [
        "loan_df[['fico_score','default_rate']].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>default_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.886782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_rate</th>\n",
              "      <td>-0.886782</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              fico_score  default_rate\n",
              "fico_score      1.000000     -0.886782\n",
              "default_rate   -0.886782      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KU_OG0PnBCF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "ceec8496-1e5b-4de9-afcb-38f894db94dc"
      },
      "source": [
        "sns.regplot(x='fico_score',y='default_rate',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4366becf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVtDbMfzoiTg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "8fafa97b-cbd2-4164-b3ab-9a16915ab14c"
      },
      "source": [
        "loan_df[['fico_score','default_rate']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fico_score</th>\n",
              "      <th>default_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fico_score</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.939511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default_rate</th>\n",
              "      <td>-0.939511</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              fico_score  default_rate\n",
              "fico_score      1.000000     -0.939511\n",
              "default_rate   -0.939511      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWyX5-YQkDWK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "d0af557f-f4d0-4f83-85b8-919e6b603b7d"
      },
      "source": [
        "loan_df[['median_annual_inc','loan_total']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>median_annual_inc</th>\n",
              "      <th>loan_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>median_annual_inc</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.035934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_total</th>\n",
              "      <td>0.035934</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   median_annual_inc  loan_total\n",
              "median_annual_inc           1.000000    0.035934\n",
              "loan_total                  0.035934    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHnvwlE7yzef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "1d1fe1e7-e422-45ae-bd6a-ad034f887d0f"
      },
      "source": [
        "sns.regplot(x='median_annual_inc',y='loan_total',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4365e3908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kOgRPs9kL1E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "716805bc-b7d4-43a1-d6f1-de0c98d1b0a9"
      },
      "source": [
        "loan_df[['dti','loan_total']].corr('spearman')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>dti</th>\n",
              "      <th>loan_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.029344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_total</th>\n",
              "      <td>0.029344</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 dti  loan_total\n",
              "dti         1.000000    0.029344\n",
              "loan_total  0.029344    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "calBv_Cxio13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "72bed333-9892-4679-efd3-354337c5f55a"
      },
      "source": [
        "sns.regplot(x='dti',y='loan_total',data=loan_df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4365cfd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LkO4TFTjDoW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "f817a93a-5d05-432c-bf25-7d984e82b87d"
      },
      "source": [
        "#@title\n",
        "coeff = np.poly1d(np.polyfit(loan_df['dti'], loan_df['loan_total'], 4))\n",
        "\n",
        "t = loan_df['dti']\n",
        "plt.plot(loan_df['dti'], loan_df['loan_total'], 'o', t, coeff(t), '-')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpQz11T-jZw0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}