Random_forest_guided_example.ipynb

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      "name": "Random forest: guided example",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/zdwhite/Thinkful-Unit-3/blob/master/Random_forest_guided_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "rCuWY98OQjlI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip freeze"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jGf8jTlP_AWJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i38cBDFl_Bsi",
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          "height": 71
        },
        "outputId": "9ad66b2c-fc86-478f-fef0-261a7ccaac60"
      },
      "cell_type": "code",
      "source": [
        "y2015 = pd.read_csv(\n",
        "    'https://www.dropbox.com/s/0so14yudedjmm5m/LoanStats3d.csv?dl=1',\n",
        "    skipinitialspace=True,\n",
        "    header=1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,19,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "HK_YZU1r_Fz-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "07809dbf-0578-49b2-acfa-3023956be901"
      },
      "cell_type": "code",
      "source": [
        "y2015.head()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>...</th>\n",
              "      <th>num_tl_90g_dpd_24m</th>\n",
              "      <th>num_tl_op_past_12m</th>\n",
              "      <th>pct_tl_nvr_dlq</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>68009401</td>\n",
              "      <td>72868139.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>14.85%</td>\n",
              "      <td>379.39</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>78.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>298100.0</td>\n",
              "      <td>31329.0</td>\n",
              "      <td>281300.0</td>\n",
              "      <td>13400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68354783</td>\n",
              "      <td>73244544.0</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.49%</td>\n",
              "      <td>298.58</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>88635.0</td>\n",
              "      <td>55387.0</td>\n",
              "      <td>12500.0</td>\n",
              "      <td>75635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68466916</td>\n",
              "      <td>73356753.0</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.49%</td>\n",
              "      <td>777.55</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>373572.0</td>\n",
              "      <td>68056.0</td>\n",
              "      <td>38400.0</td>\n",
              "      <td>82117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68466961</td>\n",
              "      <td>73356799.0</td>\n",
              "      <td>28000.0</td>\n",
              "      <td>28000.0</td>\n",
              "      <td>28000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49%</td>\n",
              "      <td>858.05</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91.7</td>\n",
              "      <td>22.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>304003.0</td>\n",
              "      <td>74920.0</td>\n",
              "      <td>41500.0</td>\n",
              "      <td>42503.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68495092</td>\n",
              "      <td>73384866.0</td>\n",
              "      <td>8650.0</td>\n",
              "      <td>8650.0</td>\n",
              "      <td>8650.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>19.89%</td>\n",
              "      <td>320.99</td>\n",
              "      <td>E</td>\n",
              "      <td>E3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38998.0</td>\n",
              "      <td>18926.0</td>\n",
              "      <td>2750.0</td>\n",
              "      <td>18248.0</td>\n",
              "    </tr>\n",
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              "<p>5 rows × 111 columns</p>\n",
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            ],
            "text/plain": [
              "         id   member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
              "0  68009401  72868139.0    16000.0      16000.0          16000.0   60 months   \n",
              "1  68354783  73244544.0     9600.0       9600.0           9600.0   36 months   \n",
              "2  68466916  73356753.0    25000.0      25000.0          25000.0   36 months   \n",
              "3  68466961  73356799.0    28000.0      28000.0          28000.0   36 months   \n",
              "4  68495092  73384866.0     8650.0       8650.0           8650.0   36 months   \n",
              "\n",
              "  int_rate  installment grade sub_grade            ...              \\\n",
              "0   14.85%       379.39     C        C5            ...               \n",
              "1    7.49%       298.58     A        A4            ...               \n",
              "2    7.49%       777.55     A        A4            ...               \n",
              "3    6.49%       858.05     A        A2            ...               \n",
              "4   19.89%       320.99     E        E3            ...               \n",
              "\n",
              "  num_tl_90g_dpd_24m num_tl_op_past_12m pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
              "0                0.0                2.0           78.9               0.0   \n",
              "1                0.0                2.0          100.0              66.7   \n",
              "2                0.0                0.0          100.0              20.0   \n",
              "3                0.0                0.0           91.7              22.2   \n",
              "4                0.0               12.0          100.0              50.0   \n",
              "\n",
              "  pub_rec_bankruptcies tax_liens tot_hi_cred_lim total_bal_ex_mort  \\\n",
              "0                  0.0       2.0        298100.0           31329.0   \n",
              "1                  0.0       0.0         88635.0           55387.0   \n",
              "2                  0.0       0.0        373572.0           68056.0   \n",
              "3                  0.0       0.0        304003.0           74920.0   \n",
              "4                  1.0       0.0         38998.0           18926.0   \n",
              "\n",
              "  total_bc_limit total_il_high_credit_limit  \n",
              "0       281300.0                    13400.0  \n",
              "1        12500.0                    75635.0  \n",
              "2        38400.0                    82117.0  \n",
              "3        41500.0                    42503.0  \n",
              "4         2750.0                    18248.0  \n",
              "\n",
              "[5 rows x 111 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "ZJ37DjU-TT8k",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XyldI_xKOi7i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "efc60152-d124-4420-a8a0-a0b20c2c1c2f"
      },
      "cell_type": "code",
      "source": [
        "####### REMEMBER THIS SHIT could be REALLY useful in the future\n",
        "categorical = y2015.select_dtypes(include=['object'])\n",
        "for i in categorical:\n",
        "    column = categorical[i]\n",
        "    print(i,column.nunique())\n",
        "    #print()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id 421097\n",
            "term 2\n",
            "int_rate 110\n",
            "grade 7\n",
            "sub_grade 35\n",
            "emp_title 120812\n",
            "emp_length 11\n",
            "home_ownership 4\n",
            "verification_status 3\n",
            "issue_d 12\n",
            "loan_status 7\n",
            "pymnt_plan 1\n",
            "url 421095\n",
            "desc 34\n",
            "purpose 14\n",
            "title 27\n",
            "zip_code 914\n",
            "addr_state 49\n",
            "earliest_cr_line 668\n",
            "revol_util 1211\n",
            "initial_list_status 2\n",
            "last_pymnt_d 25\n",
            "next_pymnt_d 4\n",
            "last_credit_pull_d 26\n",
            "application_type 2\n",
            "verification_status_joint 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nPGACVfeO8e3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Convert ID and Interest Rate to numeric.\n",
        "y2015['id'] = pd.to_numeric(y2015['id'], errors='coerce')\n",
        "y2015['int_rate'] = pd.to_numeric(y2015['int_rate'].str.strip('%'), errors='coerce')\n",
        "\n",
        "# Drop other columns with many unique variables\n",
        "y2015.drop(['url', 'emp_title', 'zip_code', 'earliest_cr_line', 'revol_util',\n",
        "            'sub_grade', 'addr_state', 'desc'], 1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0d0nnaK5P3Of",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Remove two summary rows at the end that don't actually contain data.\n",
        "y2015 = y2015[:-2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4jflYQQVO5K_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "pd.get_dummies(y2015)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YtN_9NmTR6Zl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y2015.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tjuy98EV_JYv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "6067246f-337d-422b-853a-775e27713cd6"
      },
      "cell_type": "code",
      "source": [
        "from sklearn import ensemble\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "rfc = ensemble.RandomForestClassifier()\n",
        "X = y2015.drop('loan_status', 1)\n",
        "Y = y2015['loan_status']\n",
        "X = pd.get_dummies(X)\n",
        "X = X.dropna(axis=1)\n",
        "\n",
        "cross_val_score(rfc, X, Y, cv=10)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.97941156, 0.98043267, 0.9812638 , 0.98154876, 0.97746379,\n",
              "       0.97672762, 0.93723432, 0.98071671, 0.9793621 , 0.97952786])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "4yfQtzMMSog5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "3a836f95-4ebb-465c-a17a-23a5814556d3"
      },
      "cell_type": "code",
      "source": [
        "y2015['loan_status'].unique()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Current', 'Fully Paid', 'Charged Off', 'Late (31-120 days)',\n",
              "       'In Grace Period', 'Default', 'Late (16-30 days)', nan],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "metadata": {
        "id": "6VvewGrklK3e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###DRILL: Third Attempt\n",
        "So here's your task. Get rid of as much data as possible without dropping below an average of 90% accuracy in a 10-fold cross validation.\n",
        "\n",
        "You'll want to do a few things in this process. First, dive into the data that we have and see which features are most important. This can be the raw features or the generated dummies. You may want to use PCA or correlation matrices.\n",
        "\n",
        "Can you do it without using anything related to payment amount or outstanding principal? How do you know?"
      ]
    },
    {
      "metadata": {
        "id": "q-3v0zO9lUOJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Get a quick sense of correlation\n",
        "f, ax = plt.subplots(figsize=(20,20))\n",
        "corrmat = y2015.corr()\n",
        "sns.heatmap(corrmat, vmax=.8, square=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BRY01jU8VadK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "corrmat.fillna(0,inplace=True)\n",
        "\n",
        "#Find all of the features that correlate with each other over .9\n",
        "\n",
        "Features = pd.DataFrame([])\n",
        "\n",
        "for row in corrmat:\n",
        "  for element in corrmat.loc[row:row,]:\n",
        "    if (corrmat.loc[row][element]>.9) &  (corrmat.loc[row][element] < 1):\n",
        "      #if element in Features.index.values\n",
        "      Features.loc[row,element]=corrmat.loc[row][element]\n",
        "      #print(row,element,corrmat.loc[row][element])\n",
        "\n",
        "Features      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9mHoc-TJHnAd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Try and group all correlated features\n",
        "# For instance\n",
        "# loan_amnt: {funded_amnt_inv,installment}\n",
        "# funded_amnt : {funded_amnt_inv,installment}\n",
        "# Therefor\n",
        "# funded_amnt_inv : {loan_amnt,funded_amnt,installment}\n",
        "# Inversely\n",
        "# installment : {funded_amnt_inv,loan_amnt,funded_amnt}\n",
        "## we can keep funded amnt and drop the other 3\n",
        "\n",
        "dropcs = ['loan_status','installment','loan_amnt','funded_amnt','num_sats','out_prncp_inv','total_pymnt_inv','total_rec_prncp','collection_recovery_fee','tot_hi_cred_lim','total_bal_ex_mort','total_il_high_credit_limit','num_rev_tl_bal_gt_0']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wgtCN_iyJ37e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "01bea2c6-b6eb-48bb-9f2e-4a80ce920cdc"
      },
      "cell_type": "code",
      "source": [
        "Features\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>installment</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>num_sats</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <th>total_bal_il</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>0.999994</td>\n",
              "      <td>0.941205</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>funded_amnt</th>\n",
              "      <td>0.999994</td>\n",
              "      <td>0.941205</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.941187</td>\n",
              "      <td>0.999994</td>\n",
              "      <td>0.999994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.941187</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.941205</td>\n",
              "      <td>0.941205</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.998602</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>0.964876</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.964892</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.964892</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.964876</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recoveries</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.973343</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.973343</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.974943</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bal_il</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.904226</td>\n",
              "      <td>0.958632</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.980849</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.980849</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_sats</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.998602</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.974943</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.904226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.958632</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            funded_amnt_inv  installment  loan_amnt  \\\n",
              "loan_amnt                          0.999994     0.941205        NaN   \n",
              "funded_amnt                        0.999994     0.941205        NaN   \n",
              "funded_amnt_inv                         NaN     0.941187   0.999994   \n",
              "installment                        0.941187          NaN   0.941205   \n",
              "open_acc                                NaN          NaN        NaN   \n",
              "out_prncp                               NaN          NaN        NaN   \n",
              "out_prncp_inv                           NaN          NaN        NaN   \n",
              "total_pymnt                             NaN          NaN        NaN   \n",
              "total_pymnt_inv                         NaN          NaN        NaN   \n",
              "total_rec_prncp                         NaN          NaN        NaN   \n",
              "recoveries                              NaN          NaN        NaN   \n",
              "collection_recovery_fee                 NaN          NaN        NaN   \n",
              "tot_cur_bal                             NaN          NaN        NaN   \n",
              "total_bal_il                            NaN          NaN        NaN   \n",
              "num_actv_rev_tl                         NaN          NaN        NaN   \n",
              "num_rev_tl_bal_gt_0                     NaN          NaN        NaN   \n",
              "num_sats                                NaN          NaN        NaN   \n",
              "tot_hi_cred_lim                         NaN          NaN        NaN   \n",
              "total_bal_ex_mort                       NaN          NaN        NaN   \n",
              "total_il_high_credit_limit              NaN          NaN        NaN   \n",
              "\n",
              "                            funded_amnt  num_sats  out_prncp_inv  out_prncp  \\\n",
              "loan_amnt                           NaN       NaN            NaN        NaN   \n",
              "funded_amnt                         NaN       NaN            NaN        NaN   \n",
              "funded_amnt_inv                0.999994       NaN            NaN        NaN   \n",
              "installment                    0.941205       NaN            NaN        NaN   \n",
              "open_acc                            NaN  0.998602            NaN        NaN   \n",
              "out_prncp                           NaN       NaN       0.999997        NaN   \n",
              "out_prncp_inv                       NaN       NaN            NaN   0.999997   \n",
              "total_pymnt                         NaN       NaN            NaN        NaN   \n",
              "total_pymnt_inv                     NaN       NaN            NaN        NaN   \n",
              "total_rec_prncp                     NaN       NaN            NaN        NaN   \n",
              "recoveries                          NaN       NaN            NaN        NaN   \n",
              "collection_recovery_fee             NaN       NaN            NaN        NaN   \n",
              "tot_cur_bal                         NaN       NaN            NaN        NaN   \n",
              "total_bal_il                        NaN       NaN            NaN        NaN   \n",
              "num_actv_rev_tl                     NaN       NaN            NaN        NaN   \n",
              "num_rev_tl_bal_gt_0                 NaN       NaN            NaN        NaN   \n",
              "num_sats                            NaN       NaN            NaN        NaN   \n",
              "tot_hi_cred_lim                     NaN       NaN            NaN        NaN   \n",
              "total_bal_ex_mort                   NaN       NaN            NaN        NaN   \n",
              "total_il_high_credit_limit          NaN       NaN            NaN        NaN   \n",
              "\n",
              "                            total_pymnt_inv  total_rec_prncp  total_pymnt  \\\n",
              "loan_amnt                               NaN              NaN          NaN   \n",
              "funded_amnt                             NaN              NaN          NaN   \n",
              "funded_amnt_inv                         NaN              NaN          NaN   \n",
              "installment                             NaN              NaN          NaN   \n",
              "open_acc                                NaN              NaN          NaN   \n",
              "out_prncp                               NaN              NaN          NaN   \n",
              "out_prncp_inv                           NaN              NaN          NaN   \n",
              "total_pymnt                        0.999997         0.964876          NaN   \n",
              "total_pymnt_inv                         NaN         0.964892     0.999997   \n",
              "total_rec_prncp                    0.964892              NaN     0.964876   \n",
              "recoveries                              NaN              NaN          NaN   \n",
              "collection_recovery_fee                 NaN              NaN          NaN   \n",
              "tot_cur_bal                             NaN              NaN          NaN   \n",
              "total_bal_il                            NaN              NaN          NaN   \n",
              "num_actv_rev_tl                         NaN              NaN          NaN   \n",
              "num_rev_tl_bal_gt_0                     NaN              NaN          NaN   \n",
              "num_sats                                NaN              NaN          NaN   \n",
              "tot_hi_cred_lim                         NaN              NaN          NaN   \n",
              "total_bal_ex_mort                       NaN              NaN          NaN   \n",
              "total_il_high_credit_limit              NaN              NaN          NaN   \n",
              "\n",
              "                            collection_recovery_fee  recoveries  \\\n",
              "loan_amnt                                       NaN         NaN   \n",
              "funded_amnt                                     NaN         NaN   \n",
              "funded_amnt_inv                                 NaN         NaN   \n",
              "installment                                     NaN         NaN   \n",
              "open_acc                                        NaN         NaN   \n",
              "out_prncp                                       NaN         NaN   \n",
              "out_prncp_inv                                   NaN         NaN   \n",
              "total_pymnt                                     NaN         NaN   \n",
              "total_pymnt_inv                                 NaN         NaN   \n",
              "total_rec_prncp                                 NaN         NaN   \n",
              "recoveries                                 0.973343         NaN   \n",
              "collection_recovery_fee                         NaN    0.973343   \n",
              "tot_cur_bal                                     NaN         NaN   \n",
              "total_bal_il                                    NaN         NaN   \n",
              "num_actv_rev_tl                                 NaN         NaN   \n",
              "num_rev_tl_bal_gt_0                             NaN         NaN   \n",
              "num_sats                                        NaN         NaN   \n",
              "tot_hi_cred_lim                                 NaN         NaN   \n",
              "total_bal_ex_mort                               NaN         NaN   \n",
              "total_il_high_credit_limit                      NaN         NaN   \n",
              "\n",
              "                            tot_hi_cred_lim  total_bal_ex_mort  \\\n",
              "loan_amnt                               NaN                NaN   \n",
              "funded_amnt                             NaN                NaN   \n",
              "funded_amnt_inv                         NaN                NaN   \n",
              "installment                             NaN                NaN   \n",
              "open_acc                                NaN                NaN   \n",
              "out_prncp                               NaN                NaN   \n",
              "out_prncp_inv                           NaN                NaN   \n",
              "total_pymnt                             NaN                NaN   \n",
              "total_pymnt_inv                         NaN                NaN   \n",
              "total_rec_prncp                         NaN                NaN   \n",
              "recoveries                              NaN                NaN   \n",
              "collection_recovery_fee                 NaN                NaN   \n",
              "tot_cur_bal                        0.974943                NaN   \n",
              "total_bal_il                            NaN           0.904226   \n",
              "num_actv_rev_tl                         NaN                NaN   \n",
              "num_rev_tl_bal_gt_0                     NaN                NaN   \n",
              "num_sats                                NaN                NaN   \n",
              "tot_hi_cred_lim                         NaN                NaN   \n",
              "total_bal_ex_mort                       NaN                NaN   \n",
              "total_il_high_credit_limit              NaN                NaN   \n",
              "\n",
              "                            total_il_high_credit_limit  num_rev_tl_bal_gt_0  \\\n",
              "loan_amnt                                          NaN                  NaN   \n",
              "funded_amnt                                        NaN                  NaN   \n",
              "funded_amnt_inv                                    NaN                  NaN   \n",
              "installment                                        NaN                  NaN   \n",
              "open_acc                                           NaN                  NaN   \n",
              "out_prncp                                          NaN                  NaN   \n",
              "out_prncp_inv                                      NaN                  NaN   \n",
              "total_pymnt                                        NaN                  NaN   \n",
              "total_pymnt_inv                                    NaN                  NaN   \n",
              "total_rec_prncp                                    NaN                  NaN   \n",
              "recoveries                                         NaN                  NaN   \n",
              "collection_recovery_fee                            NaN                  NaN   \n",
              "tot_cur_bal                                        NaN                  NaN   \n",
              "total_bal_il                                  0.958632                  NaN   \n",
              "num_actv_rev_tl                                    NaN             0.980849   \n",
              "num_rev_tl_bal_gt_0                                NaN                  NaN   \n",
              "num_sats                                           NaN                  NaN   \n",
              "tot_hi_cred_lim                                    NaN                  NaN   \n",
              "total_bal_ex_mort                                  NaN                  NaN   \n",
              "total_il_high_credit_limit                         NaN                  NaN   \n",
              "\n",
              "                            num_actv_rev_tl  open_acc  tot_cur_bal  \\\n",
              "loan_amnt                               NaN       NaN          NaN   \n",
              "funded_amnt                             NaN       NaN          NaN   \n",
              "funded_amnt_inv                         NaN       NaN          NaN   \n",
              "installment                             NaN       NaN          NaN   \n",
              "open_acc                                NaN       NaN          NaN   \n",
              "out_prncp                               NaN       NaN          NaN   \n",
              "out_prncp_inv                           NaN       NaN          NaN   \n",
              "total_pymnt                             NaN       NaN          NaN   \n",
              "total_pymnt_inv                         NaN       NaN          NaN   \n",
              "total_rec_prncp                         NaN       NaN          NaN   \n",
              "recoveries                              NaN       NaN          NaN   \n",
              "collection_recovery_fee                 NaN       NaN          NaN   \n",
              "tot_cur_bal                             NaN       NaN          NaN   \n",
              "total_bal_il                            NaN       NaN          NaN   \n",
              "num_actv_rev_tl                         NaN       NaN          NaN   \n",
              "num_rev_tl_bal_gt_0                0.980849       NaN          NaN   \n",
              "num_sats                                NaN  0.998602          NaN   \n",
              "tot_hi_cred_lim                         NaN       NaN     0.974943   \n",
              "total_bal_ex_mort                       NaN       NaN          NaN   \n",
              "total_il_high_credit_limit              NaN       NaN          NaN   \n",
              "\n",
              "                            total_bal_il  \n",
              "loan_amnt                            NaN  \n",
              "funded_amnt                          NaN  \n",
              "funded_amnt_inv                      NaN  \n",
              "installment                          NaN  \n",
              "open_acc                             NaN  \n",
              "out_prncp                            NaN  \n",
              "out_prncp_inv                        NaN  \n",
              "total_pymnt                          NaN  \n",
              "total_pymnt_inv                      NaN  \n",
              "total_rec_prncp                      NaN  \n",
              "recoveries                           NaN  \n",
              "collection_recovery_fee              NaN  \n",
              "tot_cur_bal                          NaN  \n",
              "total_bal_il                         NaN  \n",
              "num_actv_rev_tl                      NaN  \n",
              "num_rev_tl_bal_gt_0                  NaN  \n",
              "num_sats                             NaN  \n",
              "tot_hi_cred_lim                      NaN  \n",
              "total_bal_ex_mort               0.904226  \n",
              "total_il_high_credit_limit      0.958632  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "metadata": {
        "id": "O8isNxe0MgR1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "07187713-7418-4b9e-aa59-23600f240a49"
      },
      "cell_type": "code",
      "source": [
        "from sklearn import ensemble\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "rfc = ensemble.RandomForestClassifier()\n",
        "X = y2015.drop(dropcs, 1)\n",
        "Y = y2015['loan_status']\n",
        "X = pd.get_dummies(X)\n",
        "X = X.dropna(axis=1)\n",
        "\n",
        "cross_val_score(rfc, X, Y, cv=10)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9714089 , 0.97482843, 0.97931657, 0.97903161, 0.96896224,\n",
              "       0.97409166, 0.86639436, 0.97955306, 0.97743843, 0.97489669])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "4kKQmslFacFL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Elminiate more features!!!"
      ]
    },
    {
      "metadata": {
        "id": "LXW8CJwkZmZG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CUApTSuFZnjG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}