portfolio_optimization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/andrewzlee/prosper-investing/blob/master/portfolio_optimization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1ug-Zq2LX2_"
      },
      "source": [
        "# Prosper Loan Optimal Selection Strategy\n",
        "\n",
        "\n",
        "### Assumptions\n",
        "1.   Assume only 1 year timeframe. Not realistic\n",
        "2.   Assume defaults returns a 50% loss.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 0: Setup space, High level Default Exploration"
      ],
      "metadata": {
        "id": "-9Du6vJ32qhD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "yCegrFP0ZPz1"
      },
      "outputs": [],
      "source": [
        "#%pip install -U sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NRq9ltkT4J_",
        "outputId": "0d42081b-6e23-4f7d-eaca-ab07e8aa8fa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "path = '/content/drive/My Drive/Colab Notebooks/prosper-investing'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jXKX5GX-UFrV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "8lenaCspUWcO"
      },
      "outputs": [],
      "source": [
        "#https://stackoverflow.com/questions/62832445/is-it-okay-to-build-a-model-on-imbalanced-data\n",
        "dat_full_balanced = pd.read_pickle(path + \"/data/modeling_data.pkl\")\n",
        "dat_full = pd.read_pickle(path + \"/data/modeling_data_imbalanced.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dat_full)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eql92DcmTIfj",
        "outputId": "408df7d0-3773-4ad5-cc97-9c081d8ce839"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "419249"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see here that balanced data changes the actual distribution of defaults compared to the natural state. While this may help the model learn defaults, there is unfair pressure on the lowest Fico Ranges. For example, the $0.0$ and $0.1$ both have a 100% default rate."
      ],
      "metadata": {
        "id": "xqlT9uoB22Xr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agg_data_balanced = pd.concat([dat_full_balanced[dat_full_balanced['loan_status'] == 1].groupby(\"TUFicoRange\").sum()[[\"loan_status\"]],\n",
        "          dat_full_balanced.groupby(\"TUFicoRange\").count()[\"occupation\"].rename(\"total\")],\n",
        "          ignore_index=False, axis = 1)\n",
        "agg_data_balanced"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "TiEHwJIyHURB",
        "outputId": "31b804b1-aea6-4901-e180-9f475ae95e90"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             loan_status  total\n",
              "TUFicoRange                    \n",
              "0.0                 1299   1299\n",
              "1.0                 1831   1831\n",
              "2.0                10790  18370\n",
              "3.0                16037  28449\n",
              "4.0                16591  31032\n",
              "5.0                14593  29369\n",
              "6.0                10005  22608\n",
              "7.0                 5837  14691\n",
              "8.0                 3091   8802\n",
              "9.0                 1694   5787\n",
              "10.0                 693   3201\n",
              "11.0                 225   1089"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f4536f5-5200-4e97-a561-97f7b1b030e9\" 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>loan_status</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TUFicoRange</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1299</td>\n",
              "      <td>1299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1831</td>\n",
              "      <td>1831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>10790</td>\n",
              "      <td>18370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>16037</td>\n",
              "      <td>28449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>16591</td>\n",
              "      <td>31032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>14593</td>\n",
              "      <td>29369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>10005</td>\n",
              "      <td>22608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>5837</td>\n",
              "      <td>14691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>3091</td>\n",
              "      <td>8802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>1694</td>\n",
              "      <td>5787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>693</td>\n",
              "      <td>3201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11.0</th>\n",
              "      <td>225</td>\n",
              "      <td>1089</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f4536f5-5200-4e97-a561-97f7b1b030e9')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5f4536f5-5200-4e97-a561-97f7b1b030e9 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-5f4536f5-5200-4e97-a561-97f7b1b030e9');\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",
              "\n",
              "\n",
              "<div id=\"df-98e84a3a-8bf4-4cc4-9266-491622d049c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98e84a3a-8bf4-4cc4-9266-491622d049c9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98e84a3a-8bf4-4cc4-9266-491622d049c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agg_data_balanced[\"loan_status\"]/agg_data_balanced[\"total\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bs_fShFeI6uY",
        "outputId": "8abb0d86-e3f1-425b-95a1-638dc2d3da1e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TUFicoRange\n",
              "0.0     1.000000\n",
              "1.0     1.000000\n",
              "2.0     0.587371\n",
              "3.0     0.563710\n",
              "4.0     0.534642\n",
              "5.0     0.496884\n",
              "6.0     0.442542\n",
              "7.0     0.397318\n",
              "8.0     0.351170\n",
              "9.0     0.292725\n",
              "10.0    0.216495\n",
              "11.0    0.206612\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unbalanced dataset shows a much more realistic view of default at each Fico range."
      ],
      "metadata": {
        "id": "X-wZ5s6y3WF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agg_data = pd.concat([dat_full[dat_full['loan_status'] == 1].groupby(\"TUFicoRange\").sum()[[\"loan_status\"]],\n",
        "          dat_full.groupby(\"TUFicoRange\").count()[\"occupation\"].rename(\"total\")],\n",
        "          ignore_index=False, axis = 1)\n",
        "agg_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "rPBcrfFz6CXP",
        "outputId": "00885df3-a064-4f77-ea57-90bc6c488a9f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-85b106b6b084>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  agg_data = pd.concat([dat_full[dat_full['loan_status'] == 1].groupby(\"TUFicoRange\").sum()[[\"loan_status\"]],\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             loan_status  total\n",
              "TUFicoRange                    \n",
              "640-659             7404  36200\n",
              "660-679            11629  58734\n",
              "680-699            12387  68889\n",
              "700-719            11097  71366\n",
              "720-739             7855  59037\n",
              "740-759             4525  41761\n",
              "760-779             2479  27018\n",
              "780-799             1348  19129\n",
              "800-819              546  11107\n",
              "820-850              182   3741"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdc08925-fade-490a-96e5-41f20f9a8c26\" 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>loan_status</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TUFicoRange</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>640-659</th>\n",
              "      <td>7404</td>\n",
              "      <td>36200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>660-679</th>\n",
              "      <td>11629</td>\n",
              "      <td>58734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680-699</th>\n",
              "      <td>12387</td>\n",
              "      <td>68889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>700-719</th>\n",
              "      <td>11097</td>\n",
              "      <td>71366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720-739</th>\n",
              "      <td>7855</td>\n",
              "      <td>59037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740-759</th>\n",
              "      <td>4525</td>\n",
              "      <td>41761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>760-779</th>\n",
              "      <td>2479</td>\n",
              "      <td>27018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>780-799</th>\n",
              "      <td>1348</td>\n",
              "      <td>19129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800-819</th>\n",
              "      <td>546</td>\n",
              "      <td>11107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820-850</th>\n",
              "      <td>182</td>\n",
              "      <td>3741</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdc08925-fade-490a-96e5-41f20f9a8c26')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-cdc08925-fade-490a-96e5-41f20f9a8c26 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-cdc08925-fade-490a-96e5-41f20f9a8c26');\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",
              "\n",
              "\n",
              "<div id=\"df-397044ae-5168-4b9c-8d74-e642d71e1758\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-397044ae-5168-4b9c-8d74-e642d71e1758')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-397044ae-5168-4b9c-8d74-e642d71e1758 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agg_data[\"loan_status\"]/agg_data[\"total\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WM3_nPgYI97v",
        "outputId": "27952d0e-0966-4a4d-af1b-7c6d7c52e0b5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TUFicoRange\n",
              "640-659    0.204530\n",
              "660-679    0.197994\n",
              "680-699    0.179811\n",
              "700-719    0.155494\n",
              "720-739    0.133052\n",
              "740-759    0.108355\n",
              "760-779    0.091754\n",
              "780-799    0.070469\n",
              "800-819    0.049158\n",
              "820-850    0.048650\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorize variables\n",
        "\n",
        "from sklearn.compose import make_column_selector as selector\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "categorical_columns_selector = selector(dtype_include=object)\n",
        "categorical_columns = categorical_columns_selector(dat_full)\n",
        "\n",
        "dat_categorical = dat_full[categorical_columns]\n",
        "\n",
        "encoder = OrdinalEncoder().set_output(transform=\"pandas\")\n",
        "dat_encoded = encoder.fit_transform(dat_categorical)\n",
        "dat_encoded[:10]\n",
        "\n",
        "dat_full = dat_full.drop(dat_categorical, axis = 1).join(dat_encoded)"
      ],
      "metadata": {
        "id": "K6ubUt3uJGY-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the data into training and test\n",
        "dat_full = dat_full.dropna()\n",
        "\n",
        "x = dat_full.drop(\"loan_status\",axis=1)\n",
        "y = dat_full[\"loan_status\"]\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.20,random_state=123)"
      ],
      "metadata": {
        "id": "nq5tW2cTJYlA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1: Model Random Forest"
      ],
      "metadata": {
        "id": "vLf8JXic3qyj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Sy98k8OMaWpQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "276aa50d-3486-46b9-90ab-8f38c2f29aa3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=500, random_state=123)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500, random_state=123)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "rf_clf = RandomForestClassifier(n_estimators = 500, random_state = 123)\n",
        "rf_clf.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Despite a large training set, recall is poor at a 50% threshold. Therefore we lower the threshold down to 15% to capture defaults.\n",
        "\n",
        "Note however, this report is not entirely necessary for the next steps."
      ],
      "metadata": {
        "id": "XT4OiEG93-QE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "4Fkzjyr5UHKH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6507eb7f-4d30-4d7e-9ea6-90c2494e9b34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.57      0.70     71578\n",
            "           1       0.22      0.71      0.33     11945\n",
            "\n",
            "    accuracy                           0.59     83523\n",
            "   macro avg       0.57      0.64      0.52     83523\n",
            "weighted avg       0.82      0.59      0.65     83523\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#clf_report = classification_report(y_test,rf_clf.predict(x_test))\n",
        "clf_report = classification_report(y_test,(rf_clf.predict_proba(x_test)[:,1] >= .15).astype(int))\n",
        "\n",
        "print(\"classification report\\n\",clf_report)\n",
        "\n",
        "y_pred_test_rfc = rf_clf.predict_proba(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(y_pred_test_rfc[:,1], bins = 100)"
      ],
      "metadata": {
        "id": "-sNgjYOvWGX6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c5b121f0-1c64-47f6-b4f0-aeb4a41e3ded"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([7.010e+02, 9.890e+02, 1.183e+03, 1.269e+03, 1.377e+03, 2.064e+03,\n",
              "        1.634e+03, 1.737e+03, 1.831e+03, 1.872e+03, 2.631e+03, 2.006e+03,\n",
              "        2.106e+03, 2.115e+03, 2.150e+03, 2.754e+03, 2.135e+03, 2.081e+03,\n",
              "        2.114e+03, 2.059e+03, 2.031e+03, 2.639e+03, 1.987e+03, 1.865e+03,\n",
              "        1.838e+03, 1.731e+03, 2.243e+03, 1.713e+03, 1.656e+03, 1.535e+03,\n",
              "        1.501e+03, 1.924e+03, 1.372e+03, 1.365e+03, 1.255e+03, 1.203e+03,\n",
              "        1.567e+03, 1.112e+03, 1.076e+03, 1.036e+03, 9.950e+02, 8.850e+02,\n",
              "        1.228e+03, 8.510e+02, 8.330e+02, 7.460e+02, 7.460e+02, 8.600e+02,\n",
              "        6.770e+02, 6.310e+02, 5.520e+02, 5.170e+02, 6.510e+02, 4.190e+02,\n",
              "        4.030e+02, 3.690e+02, 3.410e+02, 3.970e+02, 2.490e+02, 2.430e+02,\n",
              "        1.900e+02, 1.800e+02, 1.650e+02, 2.150e+02, 1.440e+02, 8.700e+01,\n",
              "        1.150e+02, 6.500e+01, 7.800e+01, 4.700e+01, 3.500e+01, 3.600e+01,\n",
              "        2.400e+01, 2.800e+01, 1.800e+01, 9.000e+00, 4.000e+00, 1.200e+01,\n",
              "        4.000e+00, 2.000e+00, 6.000e+00, 2.000e+00, 0.000e+00, 0.000e+00,\n",
              "        1.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n",
              "        1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]),\n",
              " array([0.     , 0.00638, 0.01276, 0.01914, 0.02552, 0.0319 , 0.03828,\n",
              "        0.04466, 0.05104, 0.05742, 0.0638 , 0.07018, 0.07656, 0.08294,\n",
              "        0.08932, 0.0957 , 0.10208, 0.10846, 0.11484, 0.12122, 0.1276 ,\n",
              "        0.13398, 0.14036, 0.14674, 0.15312, 0.1595 , 0.16588, 0.17226,\n",
              "        0.17864, 0.18502, 0.1914 , 0.19778, 0.20416, 0.21054, 0.21692,\n",
              "        0.2233 , 0.22968, 0.23606, 0.24244, 0.24882, 0.2552 , 0.26158,\n",
              "        0.26796, 0.27434, 0.28072, 0.2871 , 0.29348, 0.29986, 0.30624,\n",
              "        0.31262, 0.319  , 0.32538, 0.33176, 0.33814, 0.34452, 0.3509 ,\n",
              "        0.35728, 0.36366, 0.37004, 0.37642, 0.3828 , 0.38918, 0.39556,\n",
              "        0.40194, 0.40832, 0.4147 , 0.42108, 0.42746, 0.43384, 0.44022,\n",
              "        0.4466 , 0.45298, 0.45936, 0.46574, 0.47212, 0.4785 , 0.48488,\n",
              "        0.49126, 0.49764, 0.50402, 0.5104 , 0.51678, 0.52316, 0.52954,\n",
              "        0.53592, 0.5423 , 0.54868, 0.55506, 0.56144, 0.56782, 0.5742 ,\n",
              "        0.58058, 0.58696, 0.59334, 0.59972, 0.6061 , 0.61248, 0.61886,\n",
              "        0.62524, 0.63162, 0.638  ]),\n",
              " <BarContainer object of 100 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: Portfolio Optimization\n",
        "Using the probabilities produced by the RF, we will compute an expected return and build strategies around them. We will compare these strategies against more human-picked strategies. Further, we will look at some Naive strategies and compare our performance to a perfect knowledge porfolio."
      ],
      "metadata": {
        "id": "0mwG9Txs4gKC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hO2z_wPEcd1T"
      },
      "outputs": [],
      "source": [
        "# Create the final table of expected returns and the actual loan_status.\n",
        "threshold = 0.15\n",
        "test_data = pd.concat([x_test.reset_index(drop=True),\n",
        "                       y_test.reset_index(drop=True),\n",
        "                       pd.DataFrame(y_pred_test_rfc[:,1], columns = [\"pred_prob\"]),\n",
        "                       pd.DataFrame((rf_clf.predict_proba(x_test)[:,1] >= threshold).astype(int), columns = [\"pred_thresh\"]),\n",
        "                       rf_clf.predict_proba(x_test)[:,0] * x_test[\"lender_yield\"].rename(\"expected_return\").reset_index(drop=True),\n",
        "                       ],\n",
        "                       ignore_index=False, axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ywVnjYUbdEZu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "761acaaf-66e9-4c55-a6c5-1475712a94eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       TUFicoRange  lender_yield  loan_status  pred_prob  pred_thresh  \\\n",
              "8273           1.0        0.3082            0      0.056            0   \n",
              "73702          2.0        0.3074            0      0.062            0   \n",
              "23937          1.0        0.3082            1      0.066            0   \n",
              "76676          2.0        0.3082            0      0.076            0   \n",
              "18992          0.0        0.3082            0      0.076            0   \n",
              "33613          3.0        0.3082            0      0.076            0   \n",
              "3462           2.0        0.3082            0      0.080            0   \n",
              "23785          4.0        0.3074            0      0.078            0   \n",
              "70159          1.0        0.3029            0      0.068            0   \n",
              "66264          1.0        0.3082            0      0.088            0   \n",
              "11502          1.0        0.3082            0      0.090            0   \n",
              "34143          1.0        0.3082            0      0.090            0   \n",
              "38306          1.0        0.3082            0      0.090            0   \n",
              "45168          1.0        0.3082            0      0.094            0   \n",
              "11575          1.0        0.3082            0      0.096            0   \n",
              "80531          2.0        0.3082            0      0.098            0   \n",
              "39360          2.0        0.3082            0      0.100            0   \n",
              "37413          1.0        0.3082            0      0.102            0   \n",
              "74142          1.0        0.3029            0      0.088            0   \n",
              "60681          3.0        0.3024            1      0.088            0   \n",
              "\n",
              "       expected_return  \n",
              "8273          0.290941  \n",
              "73702         0.288341  \n",
              "23937         0.287859  \n",
              "76676         0.284777  \n",
              "18992         0.284777  \n",
              "33613         0.284777  \n",
              "3462          0.283544  \n",
              "23785         0.283423  \n",
              "70159         0.282303  \n",
              "66264         0.281078  \n",
              "11502         0.280462  \n",
              "34143         0.280462  \n",
              "38306         0.280462  \n",
              "45168         0.279229  \n",
              "11575         0.278613  \n",
              "80531         0.277996  \n",
              "39360         0.277380  \n",
              "37413         0.276764  \n",
              "74142         0.276245  \n",
              "60681         0.275789  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e257105-94a1-4121-a360-0c733361d5d2\" 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>TUFicoRange</th>\n",
              "      <th>lender_yield</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pred_prob</th>\n",
              "      <th>pred_thresh</th>\n",
              "      <th>expected_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8273</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0</td>\n",
              "      <td>0.290941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73702</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.3074</td>\n",
              "      <td>0</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0</td>\n",
              "      <td>0.288341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23937</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>1</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0</td>\n",
              "      <td>0.287859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76676</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.076</td>\n",
              "      <td>0</td>\n",
              "      <td>0.284777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18992</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.076</td>\n",
              "      <td>0</td>\n",
              "      <td>0.284777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33613</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.076</td>\n",
              "      <td>0</td>\n",
              "      <td>0.284777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3462</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0</td>\n",
              "      <td>0.283544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23785</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.3074</td>\n",
              "      <td>0</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0</td>\n",
              "      <td>0.283423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70159</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3029</td>\n",
              "      <td>0</td>\n",
              "      <td>0.068</td>\n",
              "      <td>0</td>\n",
              "      <td>0.282303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66264</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0</td>\n",
              "      <td>0.281078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11502</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0</td>\n",
              "      <td>0.280462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34143</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0</td>\n",
              "      <td>0.280462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38306</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0</td>\n",
              "      <td>0.280462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45168</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0</td>\n",
              "      <td>0.279229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11575</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0</td>\n",
              "      <td>0.278613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80531</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.098</td>\n",
              "      <td>0</td>\n",
              "      <td>0.277996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39360</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.277380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37413</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3082</td>\n",
              "      <td>0</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.276764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74142</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3029</td>\n",
              "      <td>0</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0</td>\n",
              "      <td>0.276245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60681</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.3024</td>\n",
              "      <td>1</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0</td>\n",
              "      <td>0.275789</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e257105-94a1-4121-a360-0c733361d5d2')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5e257105-94a1-4121-a360-0c733361d5d2 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-5e257105-94a1-4121-a360-0c733361d5d2');\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",
              "\n",
              "\n",
              "<div id=\"df-c6ab2151-647d-4c6a-85d0-1e54f1de0547\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6ab2151-647d-4c6a-85d0-1e54f1de0547')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c6ab2151-647d-4c6a-85d0-1e54f1de0547 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "test_data.sort_values(by = \"expected_return\", axis = 0, ascending = False).head(20)[[\"TUFicoRange\",\"lender_yield\",\"loan_status\",\"pred_prob\",\"pred_thresh\",\"expected_return\"]]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to evaluate a selected porfolio and return it's performance\n",
        "def modelPerformance(num_invested, principal, portfolio, total_return):\n",
        "  print(\"Total Invested: \" + str(num_invested) + \" Num Defaulted: \" + str(sum(portfolio[\"loan_status\"])) + \" Pct Default: \" + str(round(sum(portfolio[\"loan_status\"]/num_invested), 4) ))\n",
        "  print(\"Average Expected Return: \" + str(round(portfolio[\"expected_return\"].mean(), 4) ))\n",
        "  print(\"Return: $\" + str(round(total_return - (principal*num_invested),4) ))\n",
        "  print(\"ROI: \" + str(round(total_return/(principal*num_invested),4) ))"
      ],
      "metadata": {
        "id": "q2oTCB1ovcXs"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Strategy\n",
        "Select random number of loans from each FICO group based on a human-picked distribution."
      ],
      "metadata": {
        "id": "aA3fytmCICxj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "distr = {\"0.0\": 0.0, \"1.0\": 0.00, \"2.0\": 0.05, \"3.0\": 0.10, \"4.0\": 0.10, \"5.0\": 0.15,\n",
        "         \"6.0\": 0.20, \"7.0\": 0.15, \"8.0\": 0.15, \"9.0\": 0.10, \"10.0\": 0.00, \"11.0\": 0.00 }\n",
        "\n",
        "# distr = {\"0.0\": 0.0, \"1.0\": 0.00, \"2.0\": 0.00, \"3.0\": 0.00, \"4.0\": 0.00, \"5.0\": 0.00,\n",
        "#          \"6.0\": 0.00, \"7.0\": 0.00, \"8.0\": 0.00, \"9.0\": 0.00, \"10.0\": 0.0, \"11.0\": 1 }\n",
        "principal = 25\n",
        "num_invested = 200\n",
        "\n",
        "distr_loans = {k: v*num_invested for k, v in distr.items()}\n",
        "\n",
        "portfolio = pd.DataFrame()\n",
        "\n",
        "for d in distr:\n",
        "  test_subset = test_data[test_data[\"TUFicoRange\"] == int(float(d))] # Select all rows for a given Fico\n",
        "  if int(distr_loans[d]) > 0 and len(test_subset) > 0:\n",
        "    test_subset = test_subset.sample(n = int(distr_loans[d]), replace = False, ignore_index = False, random_state = 123)\n",
        "    portfolio = pd.concat([portfolio, test_subset], ignore_index = True)\n",
        "\n",
        "print(len(portfolio))"
      ],
      "metadata": {
        "id": "mwZzxLjeIJ7G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c58ad12-7520-4824-d823-0fc696e24fea"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "principal = 25\n",
        "\n",
        "total_return = sum(principal * portfolio.apply(lambda row: 0.5 if row[\"loan_status\"] == 1 else 1 + row[\"lender_yield\"], axis = 1))\n",
        "\n",
        "modelPerformance(num_invested, principal, portfolio, total_return)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPbfcYKVmBg1",
        "outputId": "be2fbfa5-f9b0-45bd-9ce0-12e22cd12249"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Invested: 200 Num Defaulted: 13 Pct Default: 0.065\n",
            "Average Expected Return: 0.0799\n",
            "Return: $255.0375\n",
            "ROI: 1.051\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Highest Returns\n",
        "Select all the loans with the highest yield in a greedy approach."
      ],
      "metadata": {
        "id": "Fei4WVBS6Qg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Highest returns\n",
        "principal = 25\n",
        "num_invested = 200\n",
        "portfolio = test_data.sort_values(by = \"lender_yield\", axis = 0, ascending = False).head(num_invested)[[\"TUFicoRange\",\"lender_yield\",\"loan_status\",\"pred_prob\",\"pred_thresh\",\"expected_return\"]]\n",
        "\n",
        "total_return = sum(principal * portfolio.apply(lambda row: 0.5 if row[\"loan_status\"] == 1 else 1 + row[\"lender_yield\"], axis = 1))\n",
        "\n",
        "modelPerformance(num_invested, principal, portfolio, total_return)"
      ],
      "metadata": {
        "id": "rz_G7whOeCMz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d5bfd08-cd68-4254-b09d-a67ea61dc3b8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Invested: 200 Num Defaulted: 56 Pct Default: 0.28\n",
            "Average Expected Return: 0.2184\n",
            "Return: $409.52\n",
            "ROI: 1.0819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perfect Strategy\n",
        "Select all the loans with the highest yield like previously. However, we assume there are no defaults. This is close to only picking non-defaults, but slightly higher ROI."
      ],
      "metadata": {
        "id": "EEKssepw60lJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Perfect Strategy, no defaults\n",
        "principal = 25\n",
        "num_invested = 200\n",
        "portfolio = test_data.sort_values(by = \"lender_yield\", axis = 0, ascending = False).head(num_invested)[[\"TUFicoRange\",\"lender_yield\",\"loan_status\",\"pred_prob\",\"pred_thresh\",\"expected_return\"]]\n",
        "\n",
        "total_return = sum(principal * portfolio.apply(lambda row: 1 + row[\"lender_yield\"] if row[\"loan_status\"] == 1 else 1 + row[\"lender_yield\"], axis = 1))\n",
        "\n",
        "modelPerformance(num_invested, principal, portfolio, total_return)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flx0Ndlo8jtY",
        "outputId": "f98897ec-49cf-4479-b6fe-918a9cbf4eec"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Invested: 200 Num Defaulted: 56 Pct Default: 0.28\n",
            "Average Expected Return: 0.2184\n",
            "Return: $1541.0\n",
            "ROI: 1.3082\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optimized Strategy\n",
        "\n",
        "Highest $E(r)$ Strategies:\n",
        "1. Compute expected return for all loans and sort by highest return\n",
        "2. Sort by lowest probability of default\n",
        "\n",
        "Modified Highest $E(r)$:\n",
        "1. Define a weighting for every FICO range that punishes lower scores even further.\n",
        "2. We can also use an optimization algo to find the best weighting that maximizes return. However, the optimizer will need to know defaults ahead of time to calculate. Consequently, we could be introducing immense bias. We can try to backtest.\n"
      ],
      "metadata": {
        "id": "U37hvZM6IF9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#(1)\n",
        "principal = 25\n",
        "num_invested = 200\n",
        "portfolio = test_data.sort_values(by = \"expected_return\", axis = 0, ascending = False).head(num_invested)[[\"TUFicoRange\",\"lender_yield\",\"loan_status\",\"pred_prob\",\"pred_thresh\",\"expected_return\"]]\n",
        "\n",
        "total_return = sum(principal * portfolio.apply(lambda row: 0.5 if row[\"loan_status\"] == 1 else 1 + row[\"lender_yield\"], axis = 1))\n",
        "\n",
        "modelPerformance(num_invested, principal, portfolio, total_return)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15oCEnHw1e3D",
        "outputId": "61b2f516-4c6f-4127-c44d-50cd7203b5c6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Invested: 200 Num Defaulted: 31 Pct Default: 0.155\n",
            "Average Expected Return: 0.2649\n",
            "Return: $898.7775\n",
            "ROI: 1.1798\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Modifed (1)\n",
        "\n",
        "distr = {\"0.0\": 0.60, \"1.0\": 0.65, \"2.0\": 0.70, \"3.0\": 0.75, \"4.0\": 0.80, \"5.0\": 0.85,\n",
        "         \"6.0\": 0.93, \"7.0\": 0.95, \"8.0\": 0.96, \"9.0\": 0.98, \"10.0\": 1.0, \"11.0\": 1.0 }\n",
        "\n",
        "test_data[\"updated_return\"] = test_data.apply(lambda row: distr[str(row[\"TUFicoRange\"])] * row[\"expected_return\"], axis = 1)\n",
        "\n",
        "principal = 25\n",
        "num_invested = 200\n",
        "portfolio = test_data.sort_values(by = \"updated_return\", axis = 0, ascending = False).head(num_invested)[[\"TUFicoRange\",\"lender_yield\",\"loan_status\",\"pred_prob\",\"pred_thresh\",\"expected_return\",\"updated_return\"]]\n",
        "\n",
        "total_return = sum(principal * portfolio.apply(lambda row: 0.5 if row[\"loan_status\"] == 1 else 1 + row[\"lender_yield\"], axis = 1))\n",
        "\n",
        "\n",
        "modelPerformance(num_invested, principal, portfolio, total_return)\n"
      ],
      "metadata": {
        "id": "nLJshpzxrQS2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d766e28f-9dd4-4e96-a26a-97ebaf69f1d5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Invested: 200 Num Defaulted: 32 Pct Default: 0.16\n",
            "Average Expected Return: 0.252\n",
            "Return: $846.3225\n",
            "ROI: 1.1693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4: Test code\n",
        "Uncategorized test code"
      ],
      "metadata": {
        "id": "1Fc5Fu1w9ItR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "o9OwWtA6_6mq",
        "outputId": "c0746e1c-e810-437d-d863-d660075407ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     listing_amount  estimated_return  estimated_loss_rate  lender_yield  \\\n",
              "13           6000.0            0.0908               0.0539        0.1474   \n",
              "39          10000.0            0.1355               0.0824        0.2220   \n",
              "56          15000.0            0.1065               0.0689        0.1799   \n",
              "66          10000.0            0.0682               0.0524        0.1224   \n",
              "81           8000.0            0.0552               0.0224        0.0784   \n",
              "93          15000.0            0.0645               0.0464        0.1124   \n",
              "94          15000.0            0.0612               0.0474        0.1124   \n",
              "102         10000.0            0.0537               0.0249        0.0794   \n",
              "106         15000.0            0.0536               0.0399        0.0945   \n",
              "116         10000.0            0.1039               0.0824        0.1920   \n",
              "\n",
              "     effective_yield  borrower_rate  borrower_apr  listing_term  \\\n",
              "13            0.1447         0.1574        0.1943            36   \n",
              "39            0.2179         0.2320        0.2575            60   \n",
              "56            0.1754         0.1899        0.2275            36   \n",
              "66            0.1206         0.1324        0.1687            36   \n",
              "81            0.0776         0.0884        0.1238            36   \n",
              "93            0.1109         0.1224        0.1585            36   \n",
              "94            0.1086         0.1224        0.1453            60   \n",
              "102           0.0786         0.0894        0.1248            36   \n",
              "106           0.0935         0.1045        0.1402            36   \n",
              "116           0.1863         0.2020        0.2399            36   \n",
              "\n",
              "     listing_monthly_payment  prosper_score  ...  dti_wprosper_loan  \\\n",
              "13                    210.17            6.0  ...              0.247   \n",
              "39                    283.05            7.0  ...              0.163   \n",
              "56                    549.76            9.0  ...              0.130   \n",
              "66                    338.10            9.0  ...              0.220   \n",
              "81                    253.80            4.0  ...              0.170   \n",
              "93                    499.94           10.0  ...              0.270   \n",
              "94                    335.49            8.0  ...              0.180   \n",
              "102                   317.72            5.0  ...              0.100   \n",
              "106                   487.18           10.0  ...              0.220   \n",
              "116                   372.66            6.0  ...              0.240   \n",
              "\n",
              "     prosper_rating  employment_status_description  occupation  TUFicoRange  \\\n",
              "13              2.0                            0.0        68.0          0.0   \n",
              "39              3.0                            0.0         9.0          0.0   \n",
              "56              3.0                            0.0        37.0          0.0   \n",
              "66              2.0                            0.0        71.0          0.0   \n",
              "81              0.0                            0.0        44.0          0.0   \n",
              "93              2.0                            0.0        37.0          0.0   \n",
              "94              2.0                            0.0        51.0          0.0   \n",
              "102             0.0                            0.0        19.0          0.0   \n",
              "106             0.0                            0.0        69.0          0.0   \n",
              "116             3.0                            0.0        22.0          0.0   \n",
              "\n",
              "     loan_status  pred_prob  pred_thresh  expected_return  updated_return  \n",
              "13             0      0.136            0         0.127354        0.076412  \n",
              "39             0      0.244            1         0.167832        0.100699  \n",
              "56             1      0.336            1         0.119454        0.071672  \n",
              "66             0      0.178            1         0.100613        0.060368  \n",
              "81             0      0.120            0         0.068992        0.041395  \n",
              "93             0      0.168            1         0.093517        0.056110  \n",
              "94             0      0.090            0         0.102284        0.061370  \n",
              "102            0      0.176            1         0.065426        0.039255  \n",
              "106            1      0.130            0         0.082215        0.049329  \n",
              "116            1      0.174            1         0.158592        0.095155  \n",
              "\n",
              "[10 rows x 346 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4438209a-9e04-467c-ac1a-2bee2350233b\" 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>listing_amount</th>\n",
              "      <th>estimated_return</th>\n",
              "      <th>estimated_loss_rate</th>\n",
              "      <th>lender_yield</th>\n",
              "      <th>effective_yield</th>\n",
              "      <th>borrower_rate</th>\n",
              "      <th>borrower_apr</th>\n",
              "      <th>listing_term</th>\n",
              "      <th>listing_monthly_payment</th>\n",
              "      <th>prosper_score</th>\n",
              "      <th>...</th>\n",
              "      <th>dti_wprosper_loan</th>\n",
              "      <th>prosper_rating</th>\n",
              "      <th>employment_status_description</th>\n",
              "      <th>occupation</th>\n",
              "      <th>TUFicoRange</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pred_prob</th>\n",
              "      <th>pred_thresh</th>\n",
              "      <th>expected_return</th>\n",
              "      <th>updated_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>0.0908</td>\n",
              "      <td>0.0539</td>\n",
              "      <td>0.1474</td>\n",
              "      <td>0.1447</td>\n",
              "      <td>0.1574</td>\n",
              "      <td>0.1943</td>\n",
              "      <td>36</td>\n",
              "      <td>210.17</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.136</td>\n",
              "      <td>0</td>\n",
              "      <td>0.127354</td>\n",
              "      <td>0.076412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.1355</td>\n",
              "      <td>0.0824</td>\n",
              "      <td>0.2220</td>\n",
              "      <td>0.2179</td>\n",
              "      <td>0.2320</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>60</td>\n",
              "      <td>283.05</td>\n",
              "      <td>7.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.163</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.244</td>\n",
              "      <td>1</td>\n",
              "      <td>0.167832</td>\n",
              "      <td>0.100699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>15000.0</td>\n",
              "      <td>0.1065</td>\n",
              "      <td>0.0689</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.1754</td>\n",
              "      <td>0.1899</td>\n",
              "      <td>0.2275</td>\n",
              "      <td>36</td>\n",
              "      <td>549.76</td>\n",
              "      <td>9.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.130</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.336</td>\n",
              "      <td>1</td>\n",
              "      <td>0.119454</td>\n",
              "      <td>0.071672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.0682</td>\n",
              "      <td>0.0524</td>\n",
              "      <td>0.1224</td>\n",
              "      <td>0.1206</td>\n",
              "      <td>0.1324</td>\n",
              "      <td>0.1687</td>\n",
              "      <td>36</td>\n",
              "      <td>338.10</td>\n",
              "      <td>9.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.220</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.178</td>\n",
              "      <td>1</td>\n",
              "      <td>0.100613</td>\n",
              "      <td>0.060368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>0.0552</td>\n",
              "      <td>0.0224</td>\n",
              "      <td>0.0784</td>\n",
              "      <td>0.0776</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>36</td>\n",
              "      <td>253.80</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.170</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.120</td>\n",
              "      <td>0</td>\n",
              "      <td>0.068992</td>\n",
              "      <td>0.041395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>15000.0</td>\n",
              "      <td>0.0645</td>\n",
              "      <td>0.0464</td>\n",
              "      <td>0.1124</td>\n",
              "      <td>0.1109</td>\n",
              "      <td>0.1224</td>\n",
              "      <td>0.1585</td>\n",
              "      <td>36</td>\n",
              "      <td>499.94</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.270</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.168</td>\n",
              "      <td>1</td>\n",
              "      <td>0.093517</td>\n",
              "      <td>0.056110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>15000.0</td>\n",
              "      <td>0.0612</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>0.1124</td>\n",
              "      <td>0.1086</td>\n",
              "      <td>0.1224</td>\n",
              "      <td>0.1453</td>\n",
              "      <td>60</td>\n",
              "      <td>335.49</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.180</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0</td>\n",
              "      <td>0.102284</td>\n",
              "      <td>0.061370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.0537</td>\n",
              "      <td>0.0249</td>\n",
              "      <td>0.0794</td>\n",
              "      <td>0.0786</td>\n",
              "      <td>0.0894</td>\n",
              "      <td>0.1248</td>\n",
              "      <td>36</td>\n",
              "      <td>317.72</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.176</td>\n",
              "      <td>1</td>\n",
              "      <td>0.065426</td>\n",
              "      <td>0.039255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>15000.0</td>\n",
              "      <td>0.0536</td>\n",
              "      <td>0.0399</td>\n",
              "      <td>0.0945</td>\n",
              "      <td>0.0935</td>\n",
              "      <td>0.1045</td>\n",
              "      <td>0.1402</td>\n",
              "      <td>36</td>\n",
              "      <td>487.18</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.220</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.130</td>\n",
              "      <td>0</td>\n",
              "      <td>0.082215</td>\n",
              "      <td>0.049329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>0.1039</td>\n",
              "      <td>0.0824</td>\n",
              "      <td>0.1920</td>\n",
              "      <td>0.1863</td>\n",
              "      <td>0.2020</td>\n",
              "      <td>0.2399</td>\n",
              "      <td>36</td>\n",
              "      <td>372.66</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.240</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.174</td>\n",
              "      <td>1</td>\n",
              "      <td>0.158592</td>\n",
              "      <td>0.095155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 346 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4438209a-9e04-467c-ac1a-2bee2350233b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4438209a-9e04-467c-ac1a-2bee2350233b 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-4438209a-9e04-467c-ac1a-2bee2350233b');\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",
              "\n",
              "\n",
              "<div id=\"df-5658bd4d-8517-4d89-8fd6-d86032d80071\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5658bd4d-8517-4d89-8fd6-d86032d80071')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5658bd4d-8517-4d89-8fd6-d86032d80071 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "test_data[test_data[\"TUFicoRange\"] == 0].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "-xphaMK5GNZf",
        "outputId": "f6471af1-a5c6-400c-f764-1089a493e0b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             pred_thresh  loan_status  total\n",
              "TUFicoRange                                 \n",
              "0.0                 1354         1493   7628\n",
              "1.0                 1984         2334  12387\n",
              "2.0                 1882         2422  14375\n",
              "3.0                 1522         2254  14973\n",
              "4.0                  946         1591  12298\n",
              "5.0                  445          886   8720\n",
              "6.0                  222          512   5807\n",
              "7.0                  107          297   4145\n",
              "8.0                   37          118   2365\n",
              "9.0                   13           38    825"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-243c9377-8391-4ba9-befa-0132da8ad084\" 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>pred_thresh</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TUFicoRange</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1354</td>\n",
              "      <td>1493</td>\n",
              "      <td>7628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1984</td>\n",
              "      <td>2334</td>\n",
              "      <td>12387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>1882</td>\n",
              "      <td>2422</td>\n",
              "      <td>14375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1522</td>\n",
              "      <td>2254</td>\n",
              "      <td>14973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>946</td>\n",
              "      <td>1591</td>\n",
              "      <td>12298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>445</td>\n",
              "      <td>886</td>\n",
              "      <td>8720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>222</td>\n",
              "      <td>512</td>\n",
              "      <td>5807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>107</td>\n",
              "      <td>297</td>\n",
              "      <td>4145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>37</td>\n",
              "      <td>118</td>\n",
              "      <td>2365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>13</td>\n",
              "      <td>38</td>\n",
              "      <td>825</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-243c9377-8391-4ba9-befa-0132da8ad084')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-243c9377-8391-4ba9-befa-0132da8ad084 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-243c9377-8391-4ba9-befa-0132da8ad084');\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",
              "\n",
              "\n",
              "<div id=\"df-c1c4cff8-9722-470e-8848-1013feec8a9c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c1c4cff8-9722-470e-8848-1013feec8a9c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c1c4cff8-9722-470e-8848-1013feec8a9c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "agg_data = pd.concat([test_data[test_data['loan_status'] == 1].groupby(\"TUFicoRange\").sum()[[\"pred_thresh\", \"loan_status\"]],\n",
        "          test_data.groupby(\"TUFicoRange\").count()[\"occupation\"].rename(\"total\")],\n",
        "          ignore_index=False, axis = 1)\n",
        "agg_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNR2e1IGhdi8"
      },
      "source": [
        "These results show that as a person becomes more credit worthy, it becomes much harder to anticipate defaults."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "5LOK3krPcbFh",
        "outputId": "b1393cda-5b74-4d4b-c151-ac1bcdd7f8ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             pred_thresh  loan_status  total  pct_default    recall\n",
              "TUFicoRange                                                        \n",
              "0.0                 1354         1493   7628     0.195726  0.906899\n",
              "1.0                 1984         2334  12387     0.188423  0.850043\n",
              "2.0                 1882         2422  14375     0.168487  0.777044\n",
              "3.0                 1522         2254  14973     0.150538  0.675244\n",
              "4.0                  946         1591  12298     0.129371  0.594595\n",
              "5.0                  445          886   8720     0.101606  0.502257\n",
              "6.0                  222          512   5807     0.088169  0.433594\n",
              "7.0                  107          297   4145     0.071653  0.360269\n",
              "8.0                   37          118   2365     0.049894  0.313559\n",
              "9.0                   13           38    825     0.046061  0.342105"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6127396a-ddf1-4940-a60b-040859bb34f4\" 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>pred_thresh</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>total</th>\n",
              "      <th>pct_default</th>\n",
              "      <th>recall</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TUFicoRange</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1354</td>\n",
              "      <td>1493</td>\n",
              "      <td>7628</td>\n",
              "      <td>0.195726</td>\n",
              "      <td>0.906899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1984</td>\n",
              "      <td>2334</td>\n",
              "      <td>12387</td>\n",
              "      <td>0.188423</td>\n",
              "      <td>0.850043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>1882</td>\n",
              "      <td>2422</td>\n",
              "      <td>14375</td>\n",
              "      <td>0.168487</td>\n",
              "      <td>0.777044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1522</td>\n",
              "      <td>2254</td>\n",
              "      <td>14973</td>\n",
              "      <td>0.150538</td>\n",
              "      <td>0.675244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>946</td>\n",
              "      <td>1591</td>\n",
              "      <td>12298</td>\n",
              "      <td>0.129371</td>\n",
              "      <td>0.594595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>445</td>\n",
              "      <td>886</td>\n",
              "      <td>8720</td>\n",
              "      <td>0.101606</td>\n",
              "      <td>0.502257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>222</td>\n",
              "      <td>512</td>\n",
              "      <td>5807</td>\n",
              "      <td>0.088169</td>\n",
              "      <td>0.433594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>107</td>\n",
              "      <td>297</td>\n",
              "      <td>4145</td>\n",
              "      <td>0.071653</td>\n",
              "      <td>0.360269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>37</td>\n",
              "      <td>118</td>\n",
              "      <td>2365</td>\n",
              "      <td>0.049894</td>\n",
              "      <td>0.313559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>13</td>\n",
              "      <td>38</td>\n",
              "      <td>825</td>\n",
              "      <td>0.046061</td>\n",
              "      <td>0.342105</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6127396a-ddf1-4940-a60b-040859bb34f4')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6127396a-ddf1-4940-a60b-040859bb34f4 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-6127396a-ddf1-4940-a60b-040859bb34f4');\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",
              "\n",
              "\n",
              "<div id=\"df-838a0950-cab9-46ad-a772-36857785c95b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-838a0950-cab9-46ad-a772-36857785c95b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-838a0950-cab9-46ad-a772-36857785c95b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "agg_data[\"pct_default\"] = agg_data.apply(lambda row: row[\"loan_status\"]/ row[\"total\"],axis = 1)\n",
        "agg_data[\"recall\"] = agg_data[\"pred_thresh\"]/agg_data[\"loan_status\"] #agg_data.apply(lambda row: row[\"pred_thresh\"]/ row[\"loan_status\"],axis = 1)\n",
        "agg_data"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNIGCwZqW8m8bNoFLYUnLlo",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}