5.Model-Explanation-with-SHAP-Value.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "5a7ebe20",
   "metadata": {},
   "source": [
    "# Loan Default Prediction Model Explanation with SHAP Value\n",
    "Author: Alice Zhou"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b46600",
   "metadata": {},
   "source": [
    "Machine learning techniques are increasingly being used in the loan default prediction. After constructing a predictive model, it is important to explain the model properly and make users understand it easily.\n",
    "- Objectives: This report uses a hypothetical loan dataset for loan default prediction. After using supervised machine learning (Random Forest) to build the prediction model, SHAP values will be incorporated to explain the model. \n",
    "- Dataset: The dataset contains information on loan application details, borrower's credit history from Credit Bureau, borrower's financial query records, borrower's call records, and third-party data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651bee4c",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "- [1. Data Preparation](#1)\n",
    "    - [1.1 Feature Engineering](#11)\n",
    "        - [1.1.1 Special Values Handling](#111)\n",
    "        - [1.1.2 WOE Transformation](#112)\n",
    "    - [1.2 Train Test Split](#12)\n",
    "- [2. Modeling](#2)\n",
    "    - [2.1 RF Modeling](#21)\n",
    "    - [2.2 Top 10 Important Features](#22)\n",
    "- [3. Model Interpretation with SHAP Values](#3) \n",
    "    - [3.1 Global Interpretability - Variable Importance Plot](#31)\n",
    "    - [3.2 Global Interpretability- SHAP Dependence Plot](#32)\n",
    "    - [3.3 Local Interpretability - Individual SHAP Value Plot](#33)\n",
    "- [4. Conclusion](#4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89649946",
   "metadata": {},
   "source": [
    "## 1. Data Preparation <a class=\"anchor\" id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7d20314",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b00d8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/alicezhou/Documents/Columbia/5420 Anomaly Detection/11. Automated Machine Learning and SHAP/Assignment 11/XYZloan_default_selected_vars.csv') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4e691e",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Some basic understandings of the raw data:\n",
    "- Each raw is a borrower (id), and the other variables are the information related to this borrower.\n",
    "- For the loan default predictive model, \"loan_default\" is the target variable.\n",
    "- There are more than 80 variables that can be used as input variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b402a714",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>0.467174</td>\n",
       "      <td>1.196806</td>\n",
       "      <td>2.212313</td>\n",
       "      <td>1.33655</td>\n",
       "      <td>1.306335</td>\n",
       "      <td>...</td>\n",
       "      <td>28.007499</td>\n",
       "      <td>38.235012</td>\n",
       "      <td>33.270641</td>\n",
       "      <td>1379.553332</td>\n",
       "      <td>1245.044602</td>\n",
       "      <td>2441.503517</td>\n",
       "      <td>2172.71384</td>\n",
       "      <td>3404.975112</td>\n",
       "      <td>3005.615048</td>\n",
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       "      <td>-99.000000</td>\n",
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      "text/plain": [
       "       Unnamed: 0.1    Unnamed: 0            id  loan_default         AP001  \\\n",
       "count  80000.000000  80000.000000  80000.000000  80000.000000  80000.000000   \n",
       "mean   39999.500000  40000.500000  40000.500000      0.193600     31.706913   \n",
       "std    23094.155105  23094.155105  23094.155105      0.395121      7.075070   \n",
       "min        0.000000      1.000000      1.000000      0.000000     20.000000   \n",
       "25%    19999.750000  20000.750000  20000.750000      0.000000     27.000000   \n",
       "50%    39999.500000  40000.500000  40000.500000      0.000000     30.000000   \n",
       "75%    59999.250000  60000.250000  60000.250000      0.000000     35.000000   \n",
       "max    79999.000000  80000.000000  80000.000000      1.000000     56.000000   \n",
       "\n",
       "              AP002         AP003         AP004        AP007         AP008  \\\n",
       "count  80000.000000  80000.000000  80000.000000  80000.00000  80000.000000   \n",
       "mean       1.321813      2.014925     11.235413      3.30130      3.117200   \n",
       "std        0.467174      1.196806      2.212313      1.33655      1.306335   \n",
       "min        1.000000      1.000000      3.000000      1.00000      1.000000   \n",
       "25%        1.000000      1.000000     12.000000      2.00000      2.000000   \n",
       "50%        1.000000      1.000000     12.000000      3.00000      3.000000   \n",
       "75%        2.000000      3.000000     12.000000      5.00000      4.000000   \n",
       "max        2.000000      6.000000     12.000000      5.00000      5.000000   \n",
       "\n",
       "       ...         CD160         CD162         CD164         CD166  \\\n",
       "count  ...  79619.000000  79619.000000  79619.000000  79619.000000   \n",
       "mean   ...      6.911956     14.271694     11.773358    909.089313   \n",
       "std    ...     28.007499     38.235012     33.270641   1379.553332   \n",
       "min    ...    -99.000000    -99.000000    -99.000000    -99.000000   \n",
       "25%    ...      2.000000      5.000000      4.000000     84.000000   \n",
       "50%    ...      7.000000     12.000000     10.000000    475.000000   \n",
       "75%    ...     14.000000     23.000000     20.000000   1209.000000   \n",
       "max    ...   1061.000000   2792.000000   1579.000000  48585.000000   \n",
       "\n",
       "              CD167         CD169        CD170          CD172         CD173  \\\n",
       "count  79619.000000  79619.000000  79619.00000   79619.000000  79619.000000   \n",
       "mean     810.786219   1732.693314   1539.33443    2513.226491   2229.606137   \n",
       "std     1245.044602   2441.503517   2172.71384    3404.975112   3005.615048   \n",
       "min      -99.000000    -99.000000    -99.00000     -99.000000    -99.000000   \n",
       "25%       34.000000    309.000000    226.00000     539.000000    414.000000   \n",
       "50%      397.000000   1023.000000    870.00000    1553.000000   1324.000000   \n",
       "75%     1080.000000   2287.000000   2030.00000    3296.000000   2936.000000   \n",
       "max    29664.000000  88364.000000  54651.00000  125352.000000  87312.000000   \n",
       "\n",
       "              MB005  \n",
       "count  77207.000000  \n",
       "mean       5.976272  \n",
       "std        3.641814  \n",
       "min        0.000000  \n",
       "25%        3.000000  \n",
       "50%        5.000000  \n",
       "75%        8.000000  \n",
       "max       47.000000  \n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Simple Summary Statistics \n",
    "df.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430d7381",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "From the variable statistics, we can see that there are some special values (eg. -99) in some variables (eg. CD172, CD173). I will investigate what other special values and create categories to distinct them later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce397b63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000, 89)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "286a6e0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AP003</td>\n",
       "      <td>int64</td>\n",
       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AP004</td>\n",
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       "      <td>AP</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AP005</td>\n",
       "      <td>object</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AP006</td>\n",
       "      <td>object</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        varname   dtype source\n",
       "0  Unnamed: 0.1   int64     Un\n",
       "1    Unnamed: 0   int64     Un\n",
       "2            id   int64     id\n",
       "3  loan_default   int64     lo\n",
       "4         AP001   int64     AP\n",
       "5         AP002   int64     AP\n",
       "6         AP003   int64     AP\n",
       "7         AP004   int64     AP\n",
       "8         AP005  object     AP\n",
       "9         AP006  object     AP"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize the variables' data source\n",
    "var = pd.DataFrame(df.dtypes).reset_index()\n",
    "var.columns = ['varname','dtype'] \n",
    "var['source'] = var['varname'].str[:2]\n",
    "var.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9ae9f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    58\n",
       "int64      28\n",
       "object      3\n",
       "Name: dtype, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate variables' data type \n",
    "var['dtype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26e145fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>varname</th>\n",
       "      <th>dtype</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AP005</td>\n",
       "      <td>object</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AP006</td>\n",
       "      <td>object</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>MB007</td>\n",
       "      <td>object</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   varname   dtype source\n",
       "8    AP005  object     AP\n",
       "9    AP006  object     AP\n",
       "88   MB007  object     MB"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 3 categorical variables, and the other are numeric variables\n",
    "var[var['dtype']=='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3416494",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD    36\n",
       "TD    24\n",
       "AP     9\n",
       "CR     8\n",
       "PA     6\n",
       "Un     2\n",
       "MB     2\n",
       "id     1\n",
       "lo     1\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the data source\n",
    "var['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5eda1844",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AP001', 'AP002', 'AP003', 'AP004', 'AP006', 'AP007', 'AP008', 'AP009']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"AP005\" is dropped since it's a timestamp data, which cannot be employed in tree-based methods\n",
    "MB_list = list(var[var['source']=='MB']['varname'])\n",
    "AP_list = list(var[(var['source']=='AP') & (var['varname']!='AP005')]['varname'])\n",
    "TD_list = list(var[var['source']=='TD']['varname'])\n",
    "CR_list = list(var[var['source']=='CR']['varname'])\n",
    "PA_list = list(var[var['source']=='PA']['varname'])\n",
    "CD_list = list(var[var['source']=='CD']['varname'])\n",
    "AP_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b9e9d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = CR_list + TD_list + AP_list + MB_list + CD_list + PA_list "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae0fcbf",
   "metadata": {},
   "source": [
    "### 1.1 Feature Engineering <a class=\"anchor\" id=\"11\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b5b0a5",
   "metadata": {},
   "source": [
    "#### 1.1.1 Special Values Handling <a class=\"anchor\" id=\"111\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf752fe",
   "metadata": {},
   "source": [
    "Next, I will investigate the special values, including missing values (NA), '-99', '-98', '-1'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ce3f537",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TD022',\n",
       " 'TD023',\n",
       " 'TD024',\n",
       " 'TD025',\n",
       " 'TD026',\n",
       " 'TD027',\n",
       " 'TD028',\n",
       " 'TD029',\n",
       " 'TD044',\n",
       " 'TD048',\n",
       " 'TD051',\n",
       " 'TD054',\n",
       " 'TD055',\n",
       " 'TD061',\n",
       " 'TD062',\n",
       " 'MB005',\n",
       " 'CD008',\n",
       " 'CD018',\n",
       " 'CD071',\n",
       " 'CD072',\n",
       " 'CD088',\n",
       " 'CD100',\n",
       " 'CD101',\n",
       " 'CD106',\n",
       " 'CD107',\n",
       " 'CD108',\n",
       " 'CD113',\n",
       " 'CD114',\n",
       " 'CD115',\n",
       " 'CD117',\n",
       " 'CD118',\n",
       " 'CD120',\n",
       " 'CD121',\n",
       " 'CD123',\n",
       " 'CD130',\n",
       " 'CD131',\n",
       " 'CD132',\n",
       " 'CD133',\n",
       " 'CD135',\n",
       " 'CD136',\n",
       " 'CD137',\n",
       " 'CD152',\n",
       " 'CD153',\n",
       " 'CD160',\n",
       " 'CD162',\n",
       " 'CD164',\n",
       " 'CD166',\n",
       " 'CD167',\n",
       " 'CD169',\n",
       " 'CD170',\n",
       " 'CD172',\n",
       " 'CD173',\n",
       " 'PA022',\n",
       " 'PA023',\n",
       " 'PA028',\n",
       " 'PA029',\n",
       " 'PA030',\n",
       " 'PA031']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values\n",
    "varmissing_df = df[predictors].isnull().sum().to_frame(name='Missing Values').reset_index().rename(columns={'index': 'Variable'})\n",
    "var_to_WOE1 =varmissing_df[varmissing_df['Missing Values']!=0]['Variable'].tolist()\n",
    "var_to_WOE1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "319f6c13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CR004     1.0\n",
       "CR005     1.0\n",
       "CR009     0.0\n",
       "CR012     0.0\n",
       "CR015     2.0\n",
       "         ... \n",
       "PA023   -99.0\n",
       "PA028   -99.0\n",
       "PA029   -99.0\n",
       "PA030   -99.0\n",
       "PA031   -99.0\n",
       "Name: min, Length: 82, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether minimum values cover negative values like '-99' \n",
    "df[predictors].describe().loc['min']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7938d11",
   "metadata": {},
   "source": [
    "‘-99’, '-98' and '-1' are very likely missing values and set by the systems as extreme values. In a parametric regression, we can not use them blindly as numeric values. I am going to bin the continuous variable, leaving the special values ‘0’, ‘-99’, and’NA’ as their categories. These numeric variables will be converted to catogorical variables and I will then conduct WOE transformation for these catogorical variables for better capture their patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7d0e116",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "0\n",
      "50639\n",
      "52173\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA022']==-99))\n",
    "print(sum(df['PA022']==-98))\n",
    "print(sum(df['PA022']==-1))\n",
    "print(sum(df['PA022']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75d4b85c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "0\n",
      "56004\n",
      "57538\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA023']==-99))\n",
    "print(sum(df['PA023']==-98))\n",
    "print(sum(df['PA023']==-1))\n",
    "print(sum(df['PA023']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6fd71ecb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "50639\n",
      "0\n",
      "52173\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA028']==-99))\n",
    "print(sum(df['PA028']==-98))\n",
    "print(sum(df['PA028']==-1))\n",
    "print(sum(df['PA028']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75a36522",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "53086\n",
      "0\n",
      "54620\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA029']==-99))\n",
    "print(sum(df['PA029']==-98))\n",
    "print(sum(df['PA029']==-1))\n",
    "print(sum(df['PA029']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b47f63b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "67886\n",
      "0\n",
      "69420\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA030']==-99))\n",
    "print(sum(df['PA030']==-98))\n",
    "print(sum(df['PA030']==-1))\n",
    "print(sum(df['PA030']<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1b5c0e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1534\n",
      "56004\n",
      "0\n",
      "57538\n"
     ]
    }
   ],
   "source": [
    "# Check the other special variables\n",
    "print(sum(df['PA031']==-99))\n",
    "print(sum(df['PA031']==-98))\n",
    "print(sum(df['PA031']==-1))\n",
    "print(sum(df['PA031']<0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3352b7df",
   "metadata": {},
   "source": [
    "I will include the categorical variables for future WOE transformation as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a0463c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AP006', 'MB007']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract categorical variables\n",
    "var_to_WOE_cat = var[var['dtype']=='object']['varname'].tolist()\n",
    "var_to_WOE_cat.remove('AP005') # \"AP005\" is dropped since it's a timestamp data\n",
    "var_to_WOE_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45e8125",
   "metadata": {},
   "source": [
    "I will also include 'AP002' (CODE_GENDER) and 'AP003' (CODE_EDUCATION) for WOE transformation because these variables are also categorical information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "248b49a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TD022',\n",
       " 'TD023',\n",
       " 'TD024',\n",
       " 'TD025',\n",
       " 'TD026',\n",
       " 'TD027',\n",
       " 'TD028',\n",
       " 'TD029',\n",
       " 'TD044',\n",
       " 'TD048',\n",
       " 'TD051',\n",
       " 'TD054',\n",
       " 'TD055',\n",
       " 'TD061',\n",
       " 'TD062',\n",
       " 'MB005',\n",
       " 'CD008',\n",
       " 'CD018',\n",
       " 'CD071',\n",
       " 'CD072',\n",
       " 'CD088',\n",
       " 'CD100',\n",
       " 'CD101',\n",
       " 'CD106',\n",
       " 'CD107',\n",
       " 'CD108',\n",
       " 'CD113',\n",
       " 'CD114',\n",
       " 'CD115',\n",
       " 'CD117',\n",
       " 'CD118',\n",
       " 'CD120',\n",
       " 'CD121',\n",
       " 'CD123',\n",
       " 'CD130',\n",
       " 'CD131',\n",
       " 'CD132',\n",
       " 'CD133',\n",
       " 'CD135',\n",
       " 'CD136',\n",
       " 'CD137',\n",
       " 'CD152',\n",
       " 'CD153',\n",
       " 'CD160',\n",
       " 'CD162',\n",
       " 'CD164',\n",
       " 'CD166',\n",
       " 'CD167',\n",
       " 'CD169',\n",
       " 'CD170',\n",
       " 'CD172',\n",
       " 'CD173',\n",
       " 'PA022',\n",
       " 'PA023',\n",
       " 'PA028',\n",
       " 'PA029',\n",
       " 'PA030',\n",
       " 'PA031',\n",
       " 'AP002',\n",
       " 'AP003']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_to_WOE_num = var_to_WOE1 + ['AP002', 'AP003']\n",
    "var_to_WOE_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "872a33e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/67399853.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n"
     ]
    }
   ],
   "source": [
    "# Bin the continuous variable\n",
    "def binning(df, var):\n",
    "    # Check for missing values and replace with a default value if necessary\n",
    "    df[var] = df[var].fillna(\"-999\")\n",
    "\n",
    "    # Convert the column to a numeric type (if it is not already)\n",
    "    df[var] = pd.to_numeric(df[var], errors='coerce')\n",
    "\n",
    "    # Check if the column contains NaN values after conversion\n",
    "    if df[var].isnull().any():\n",
    "        print(f\"Warning: {var} contains NaN values after conversion. Check the data.\")\n",
    "\n",
    "    # Perform qcut on the numeric data\n",
    "    if not df[var].empty:\n",
    "        df[var + '_bin'] = pd.qcut(df[var], 10, duplicates='drop').values.add_categories(\"NoData\")\n",
    "        df[var + '_bin'] = df[var + '_bin'].fillna(\"NoData\").astype(str)\n",
    "        df[var + '_bin'].value_counts(dropna=False)\n",
    "    \n",
    "    # Handle specific values like -99, -98, and -1\n",
    "    df.loc[df[var] == -999, var + '_bin'] = \"NoData\"\n",
    "    df.loc[df[var] == -99, var + '_bin'] = -99\n",
    "    df.loc[df[var] == -98, var + '_bin'] = -98\n",
    "    df.loc[df[var] == -1, var + '_bin'] = -1\n",
    "\n",
    "# Apply binning to train and test data\n",
    "for i in var_to_WOE_num:\n",
    "    binning(df, i)\n",
    "\n",
    "var_to_WOE_num_bin = [f + '_bin' for f in var_to_WOE_num]\n",
    "features_bin = var_to_WOE_num_bin + list(var_to_WOE_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01a2cce",
   "metadata": {},
   "source": [
    "#### 1.1.2 WOE Transformation <a class=\"anchor\" id=\"112\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a4265b",
   "metadata": {},
   "source": [
    "Weight of evidence (WOE) transformation is a widely used technique in credit risk modeling or called the probability of default modeling (such models predict how capable an applicant can repay the loan). The goal of WOE transformation is to get the maximum difference among the binned categories relating to the target variable. In this transformation, the information of the target variable has been utilized. The dataset contains many categorical variables with many categories. These categories are distributed very differently are have various significant impact to the possibility of default as discussed above. Therefore, WOE is a good choice for feature engineering here. \n",
    "\n",
    "WOE encoding maps each category of the original variable to a continuous value that represents the relationship between that category and the likelihood of the target variable (loan default). The WOE values indicate how much more or less likely a particular education code is to experience the target event (loan default) compared to the overall population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09958a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n",
      "/var/folders/94/_mqchpnd0kv5pd779q9n49jh0000gn/T/ipykernel_18914/1477488107.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[var + '_WOE'] = X_cleaned\n"
     ]
    }
   ],
   "source": [
    "# Calculate WOE for each category \n",
    "import category_encoders as ce\n",
    "\n",
    "ec = ce.WOEEncoder()\n",
    "\n",
    "def WOE(df, var):\n",
    "    df[var] = df[var].fillna('NoData')\n",
    "    X = df[var]\n",
    "    y = df['loan_default']\n",
    "    X_WOE = ec.fit(X, y)\n",
    "    X_cleaned = ec.transform(X).round(2)\n",
    "    df[var + '_WOE'] = X_cleaned \n",
    "    \n",
    "for i in features_bin:\n",
    "    WOE(df, i)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0d4bbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# After WOE transformation, the original variables will be replaced by their WOE \n",
    "var_to_drop = var_to_WOE_num + var_to_WOE_cat\n",
    "var_WOE = df.filter(like='_WOE').columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66f6b45d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CR004',\n",
       " 'CR005',\n",
       " 'CR009',\n",
       " 'CR012',\n",
       " 'CR015',\n",
       " 'CR017',\n",
       " 'CR018',\n",
       " 'CR019',\n",
       " 'TD001',\n",
       " 'TD002',\n",
       " 'TD005',\n",
       " 'TD006',\n",
       " 'TD009',\n",
       " 'TD010',\n",
       " 'TD013',\n",
       " 'TD014',\n",
       " 'TD015',\n",
       " 'AP001',\n",
       " 'AP004',\n",
       " 'AP007',\n",
       " 'AP008',\n",
       " 'AP009',\n",
       " 'TD022_bin_WOE',\n",
       " 'TD023_bin_WOE',\n",
       " 'TD024_bin_WOE',\n",
       " 'TD025_bin_WOE',\n",
       " 'TD026_bin_WOE',\n",
       " 'TD027_bin_WOE',\n",
       " 'TD028_bin_WOE',\n",
       " 'TD029_bin_WOE',\n",
       " 'TD044_bin_WOE',\n",
       " 'TD048_bin_WOE',\n",
       " 'TD051_bin_WOE',\n",
       " 'TD054_bin_WOE',\n",
       " 'TD055_bin_WOE',\n",
       " 'TD061_bin_WOE',\n",
       " 'TD062_bin_WOE',\n",
       " 'MB005_bin_WOE',\n",
       " 'CD008_bin_WOE',\n",
       " 'CD018_bin_WOE',\n",
       " 'CD071_bin_WOE',\n",
       " 'CD072_bin_WOE',\n",
       " 'CD088_bin_WOE',\n",
       " 'CD100_bin_WOE',\n",
       " 'CD101_bin_WOE',\n",
       " 'CD106_bin_WOE',\n",
       " 'CD107_bin_WOE',\n",
       " 'CD108_bin_WOE',\n",
       " 'CD113_bin_WOE',\n",
       " 'CD114_bin_WOE',\n",
       " 'CD115_bin_WOE',\n",
       " 'CD117_bin_WOE',\n",
       " 'CD118_bin_WOE',\n",
       " 'CD120_bin_WOE',\n",
       " 'CD121_bin_WOE',\n",
       " 'CD123_bin_WOE',\n",
       " 'CD130_bin_WOE',\n",
       " 'CD131_bin_WOE',\n",
       " 'CD132_bin_WOE',\n",
       " 'CD133_bin_WOE',\n",
       " 'CD135_bin_WOE',\n",
       " 'CD136_bin_WOE',\n",
       " 'CD137_bin_WOE',\n",
       " 'CD152_bin_WOE',\n",
       " 'CD153_bin_WOE',\n",
       " 'CD160_bin_WOE',\n",
       " 'CD162_bin_WOE',\n",
       " 'CD164_bin_WOE',\n",
       " 'CD166_bin_WOE',\n",
       " 'CD167_bin_WOE',\n",
       " 'CD169_bin_WOE',\n",
       " 'CD170_bin_WOE',\n",
       " 'CD172_bin_WOE',\n",
       " 'CD173_bin_WOE',\n",
       " 'PA022_bin_WOE',\n",
       " 'PA023_bin_WOE',\n",
       " 'PA028_bin_WOE',\n",
       " 'PA029_bin_WOE',\n",
       " 'PA030_bin_WOE',\n",
       " 'PA031_bin_WOE',\n",
       " 'AP002_bin_WOE',\n",
       " 'AP003_bin_WOE',\n",
       " 'AP006_WOE',\n",
       " 'MB007_WOE']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictors will be the original variables without WOE transformation and new WOE variables\n",
    "predictors_fe = [x for x in predictors if x not in var_to_drop]\n",
    "predictors_fe = predictors_fe + var_WOE\n",
    "predictors_fe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb13cb2",
   "metadata": {},
   "source": [
    "### 1.2 Train Test Split <a class=\"anchor\" id=\"12\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6e2cdf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['loan_default']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f96d89a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep the variables after feature engineering for modeling \n",
    "X=df[predictors_fe] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cde2ec0f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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>MB007</th>\n",
       "      <th>MB007_WOE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IPHONE7</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WEB</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WEB</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OPPO</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WEB</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79995</th>\n",
       "      <td>OPPO</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79996</th>\n",
       "      <td>IPHONE7</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79997</th>\n",
       "      <td>VIVO</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79998</th>\n",
       "      <td>XIAOMI</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79999</th>\n",
       "      <td>WEB</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         MB007  MB007_WOE\n",
       "0      IPHONE7      -0.08\n",
       "1          WEB      -0.04\n",
       "2          WEB      -0.04\n",
       "3         OPPO       0.30\n",
       "4          WEB      -0.04\n",
       "...        ...        ...\n",
       "79995     OPPO       0.30\n",
       "79996  IPHONE7      -0.08\n",
       "79997     VIVO       0.29\n",
       "79998   XIAOMI       0.06\n",
       "79999      WEB      -0.04\n",
       "\n",
       "[80000 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MB007','MB007_WOE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "653ccf19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87a42766",
   "metadata": {},
   "source": [
    "## 2. Modeling <a class=\"anchor\" id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c14312f",
   "metadata": {},
   "source": [
    "### 2.1 RF Modeling <a class=\"anchor\" id=\"21\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3293cc74",
   "metadata": {},
   "source": [
    "I am going to use scikit-learn to run random forest model and check the variable importance based on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ef2a3e69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "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>RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)</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\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcae21ff",
   "metadata": {},
   "source": [
    "### 2.2 Top 10 Important Features <a class=\"anchor\" id=\"22\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b96885bd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.36937684e-03 1.96309931e-03 1.65642666e-02 0.00000000e+00\n",
      " 4.47447211e-02 1.48424148e-03 2.50776984e-03 3.74914778e-03\n",
      " 6.67112246e-03 5.64666978e-03 1.78658310e-02 1.96039944e-03\n",
      " 3.03313644e-02 6.46200215e-03 3.15972443e-01 5.21270486e-03\n",
      " 6.93758931e-04 7.73977167e-03 1.97326879e-01 7.80090460e-03\n",
      " 1.03745491e-03 1.02630371e-03 1.60055098e-03 9.74751390e-04\n",
      " 1.88960356e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.26024639e-03 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 2.10769711e-02 3.07127541e-03 6.78005815e-04\n",
      " 4.66907350e-04 2.55016127e-03 1.04260919e-03 3.69810149e-03\n",
      " 8.76655424e-04 5.03064092e-03 4.11864685e-03 8.90444106e-03\n",
      " 6.55242596e-03 9.81195535e-03 2.83335644e-03 4.82865410e-03\n",
      " 3.11797097e-03 1.11130847e-02 1.78605634e-03 1.92652196e-02\n",
      " 2.90834251e-03 5.48692391e-04 9.99200636e-04 8.75823547e-04\n",
      " 3.39321674e-04 1.83111742e-03 1.28401189e-03 4.55025225e-03\n",
      " 5.28241646e-04 2.68217705e-03 1.97755598e-04 3.98101990e-03\n",
      " 2.45882893e-03 2.90850605e-03 8.77123744e-04 8.10131676e-04\n",
      " 4.10949132e-04 5.02882246e-04 4.94206131e-04 5.23283843e-03\n",
      " 2.96688639e-02 8.48675858e-03 2.04125086e-03 1.50071140e-02\n",
      " 0.00000000e+00 8.35381156e-02 0.00000000e+00 3.21579498e-02]\n"
     ]
    }
   ],
   "source": [
    "# Check the variable importance values\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e512424",
   "metadata": {},
   "source": [
    "Based on the variable importance values, I am going to select the top 10 important variables for coming interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1410c97",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "features = X_train.columns\n",
    "\n",
    "# Extracting the top 10 important variables\n",
    "top_n = 10\n",
    "top_indices = indices[-top_n:]\n",
    "top_importances = importances[top_indices]\n",
    "top_features = [features[i] for i in top_indices]\n",
    "\n",
    "plt.title('Top 10 Feature Importances')\n",
    "plt.barh(range(len(top_indices)), top_importances, color='b', align='center')\n",
    "plt.yticks(range(len(top_indices)), top_features)\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "58ae0192",
   "metadata": {},
   "source": [
    "For the top 10 features, here are their business descriptions:\n",
    "1. TD013 - TD_CNT_QUERY_LAST_6MON_P2P: Total count of queries related to peer-to-peer (P2P) lending in the last 6 months.\n",
    "2. AP004 - LOAN_TERM: The term or duration of the loan.\n",
    "3. AP003_bin_WOE: The transformed and binned version of the original CODE_EDUCATION variable using Weight of Evidence (WOE) encoding. WOE encoding maps each category of the CODE_EDUCATION variable to a continuous value that represents the relationship between that category and the likelihood of the target variable (e.g., loan default). The WOE values indicate how much more or less likely a particular education code is to experience the target event (loan default) compared to the overall population.\n",
    "    - CODE_EDUCATION: Code representing the level of education of the borrower.\n",
    "4. CR015 - MONTH_CREDIT_CARD_MOB_MAX: The maximum mobile credit card usage amount in a month.\n",
    "5. MB007_WOE: The transformed version of the original MOBILE_BRAND variable using WOE encoding. \n",
    "    - MOBILE_BRAND: The brand or manufacturer of the borrower's mobile device.\n",
    "6. TD009 - TD_CNT_QUERY_LAST_3MON_P2P: Total count of queries related to peer-to-peer (P2P) lending in the last 3 months.\n",
    "7. PA028_bin_WOE: The transformed and binned version of the original AVG_LEN_COLLECTION_OR_HIGH_RISK_CALLS variable using WOE encoding. \n",
    "    - AVG_LEN_COLLECTION_OR_HIGH_RISK_CALLS: Average length of collection or high-risk calls made by the borrower.\n",
    "8. MB005_bin_WOE: The transformed and binned version of the original YR_PHONE_ACTIVE variable using Weight of Evidence (WOE) encoding. \n",
    "    - YR_PHONE_ACTIVE: Number of years the borrower's phone has been active.\n",
    "9. CD123_bin_WOE: The transformed and binned version of the original CNT_DISTINCT_OUTBOUND_CALLS_LAST_3MON variable using WOE encoding. \n",
    "    - CNT_DISTINCT_OUTBOUND_CALLS_LAST_3MON: Count of distinct outbound calls made by the borrower in the last 3 months.\n",
    "10. TD005 - TD_CNT_QUERY_LAST_1MON_P2P: Total count of queries related to peer-to-peer (P2P) lending in the last 1 month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eacaf01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TD013</th>\n",
       "      <th>AP004</th>\n",
       "      <th>AP003_bin_WOE</th>\n",
       "      <th>CR015</th>\n",
       "      <th>MB007_WOE</th>\n",
       "      <th>TD009</th>\n",
       "      <th>PA028_bin_WOE</th>\n",
       "      <th>MB005_bin_WOE</th>\n",
       "      <th>CD123_bin_WOE</th>\n",
       "      <th>TD005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78190</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13128</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33383</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>6</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50149</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>8</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24483</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>4</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72933</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13063</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>6</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54313</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14042</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TD013  AP004  AP003_bin_WOE  CR015  MB007_WOE  TD009  PA028_bin_WOE  \\\n",
       "78190      1     12          -0.51      6      -0.15      1          -0.15   \n",
       "1398       4     12           0.07      5      -0.07      4          -0.15   \n",
       "13128      5     12          -0.51      6      -0.04      4          -0.15   \n",
       "33383      3     12          -0.51      6       0.08      2          -0.15   \n",
       "50149     14     12           0.07      5      -0.04      8           0.36   \n",
       "...      ...    ...            ...    ...        ...    ...            ...   \n",
       "24483      7     12           0.07      4       0.07      7          -0.15   \n",
       "72933      2     12          -0.51      6      -0.04      2          -0.15   \n",
       "13063     11     12           0.07      5       0.29      6           0.17   \n",
       "54313      6     12           0.07      5      -0.15      7          -0.15   \n",
       "14042     13     12           0.07      5      -0.07      3          -0.15   \n",
       "\n",
       "       MB005_bin_WOE  CD123_bin_WOE  TD005  \n",
       "78190           0.22          -0.37      1  \n",
       "1398            0.16          -0.16      3  \n",
       "13128           0.16          -0.07      4  \n",
       "33383          -0.23          -0.07      0  \n",
       "50149           0.22           0.08      2  \n",
       "...              ...            ...    ...  \n",
       "24483          -0.06           0.08      4  \n",
       "72933          -0.13          -0.11      1  \n",
       "13063          -0.12           0.08      4  \n",
       "54313           0.16          -0.07      3  \n",
       "14042           0.12          -0.11      3  \n",
       "\n",
       "[64000 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the top 10 important variables\n",
    "X_train = X_train[[\"TD013\",\"AP004\",\"AP003_bin_WOE\",\"CR015\",\"MB007_WOE\",\"TD009\",\"PA028_bin_WOE\",\"MB005_bin_WOE\",\"CD123_bin_WOE\",\"TD005\"]]\n",
    "X_test = X_test[[\"TD013\",\"AP004\",\"AP003_bin_WOE\",\"CR015\",\"MB007_WOE\",\"TD009\",\"PA028_bin_WOE\",\"MB005_bin_WOE\",\"CD123_bin_WOE\",\"TD005\"]]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46b7c362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=6, n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the top 10 important variables for RF modeling and interpretation\n",
    "model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)\n",
    "model.fit(X_train, Y_train)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fd5934",
   "metadata": {},
   "source": [
    "## 3. Model Interpretation with SHAP Values <a class=\"anchor\" id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a4645b",
   "metadata": {},
   "source": [
    "SHAP (SHapley Additive exPlanations) is a method for interpreting the predictions of machine learning models. It provides a way to understand the contribution of each feature to a particular prediction. SHAP values are based on cooperative game theory and offer a rigorous and consistent way to distribute the importance of each feature across all predictions made by the model.\n",
    "\n",
    "For a loan prediction project using a random forest model, SHAP values are suitable for interpretation for the following reasons:\n",
    "\n",
    "1. Global Interpretability - Feature Importance: SHAP values can help determine the relative importance of each feature in the model's decision-making process. This is crucial for a loan prediction model as it allows you to understand which features have the most significant impact on the loan approval or rejection process. Knowing the most influential features can guide you in focusing on the relevant factors during the loan application process.\n",
    "\n",
    "2. Local Interpretability - Individual Predictions: SHAP values offer a granular view of how each feature affects individual predictions. For each loan application, you can understand the specific reasons behind the model's decision. This level of interpretability is essential in a loan prediction scenario, as it allows you to provide clear explanations to applicants about why their loan was approved or denied based on their unique circumstances.\n",
    "\n",
    "3. Consistency and Fairness: SHAP values adhere to several desirable properties, including consistency, meaning the attribution values add up to the model's overall prediction. This is essential for fairness and non-discrimination in loan approval decisions. By using SHAP values, you can ensure that the model is treating all applicants fairly and that the reasons for approval or denial align with the model's overall decision-making process.\n",
    "\n",
    "4. Model Transparency and Trust: Loan approval decisions can have significant impacts on people's lives, so it's essential to build models that are transparent and trustworthy. SHAP values provide a comprehensible and interpretable way to explain model predictions, increasing the overall transparency of the model and building trust with users and stakeholders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "133cdd95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n"
     ]
    }
   ],
   "source": [
    "# Produce SHAP values for the top 10 important features in the above RF model\n",
    "import shap\n",
    "shap_values = shap.TreeExplainer(model).shap_values(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0344093",
   "metadata": {},
   "source": [
    "### 3.1 Global Interpretability - Variable Importance Plot <a class=\"anchor\" id=\"31\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46082682",
   "metadata": {},
   "source": [
    "We are going to produce the variable importance plot. A variable importance plot lists the most significant variables in descending order. The top variables contribute more to the model than the bottom ones and thus have high predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4acadc26",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, X_train, plot_type=\"bar\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8c217490",
   "metadata": {},
   "source": [
    "The above variable importance plot ranks the feature importance in descending order. \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) shows the largest contribution to the model, and \"TD005\" (total count of queries related to peer-to-peer (P2P) lending in the last 1 month) has the lowest prediction power."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053449a8",
   "metadata": {},
   "source": [
    "The below SHAP value plot can show the positive and negative relationships of the predictors with the target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ae81583",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, X_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd512c96",
   "metadata": {},
   "source": [
    "This plot is made of all the dots in the train data. It delivers the following information:\n",
    "- Feature importance: Variables are ranked in descending order.\n",
    "- Impact: The horizontal location shows whether the effect of that value is associated with a higher or lower prediction.\n",
    "- Original value: Color shows whether that variable is high (in red) or low (in blue) for that observation.\n",
    "- Correlation: A high number of \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) has a high and positive impact on the possibility of loan default. The “high” comes from the red color, and the “positive” impact is shown on the X-axis. Similarly, we can say the \"CR015\" (the maximum mobile credit card usage amount in a month) is negatively correlated with the target variable. Other than \"CR015\", all the other variables are positively correlated with the target variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63ae06d",
   "metadata": {},
   "source": [
    "### 3.2 Global Interpretability- SHAP Dependence Plot <a class=\"anchor\" id=\"32\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e21845",
   "metadata": {},
   "source": [
    "You may ask how to show a partial dependence plot. A partial dependence plot shows the marginal effect of one or two features on the predicted outcome of a machine learning model. It tells whether the relationship between the target and a feature is linear, monotonic or more complex. As a result, this information can be valuable in the loan default prediction project, as it helps identify important features that contribute to the model's decision-making process. Lenders or financial institutions can use this insight to better assess the creditworthiness of borrowers and make more informed lending decisions. They might consider implementing different risk mitigation strategies or adjusting loan terms based on the observed interactions between these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "72306613",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we pass \"TD013\" instead of an index because dependence_plot() will find it in X's column names for us\n",
    "# \"AP004\" was automatically chosen for coloring based on a potential interaction to check that \n",
    "# the interaction is really in the model see SHAP interaction values below\n",
    "shap.dependence_plot(\"TD013\", shap_values, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc3d657",
   "metadata": {},
   "source": [
    "- The above plot shows there is an approximately linear and positive trend between “TD013” (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) and the target variable (loan default), and “TD013” highly interacts with “AP004” (the term or duration of the loan in months) frequently. \n",
    "- It suggests that borrowers with more queries about P2P lending in the last 6 months are at higher risk of loan defaulting. \n",
    "- Additionally, the strong interaction between \"TD013\" (total count of P2P lending queries) and \"AP004\" (loan term) indicates that the effect of \"TD013\" on loan default is related to the value of \"AP004\" (loan term). This interaction suggests that the relationship between P2P lending query numbers and loan default may vary based on the loan term. For example, for certain loan terms, a higher count of queries related to P2P lending might have a stronger impact on the probability of loan default compared to other loan terms. \n",
    "- This insight can help lenders make more accurate risk assessments and tailor lending decisions based on the borrower's query behavior and loan term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5e73804d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"AP004\", shap_values, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1a3666",
   "metadata": {},
   "source": [
    "- The above plot demonstrates that as the \"AP004\" (loan term) increases, there is an approximate positive trend with the target variable, indicating that certain longer-term loans might be associated with a higher probability of loan default. This suggests that borrowers with longer loan terms might have a higher risk of defaulting on their loans.\n",
    "- The frequent interaction between \"AP004\" (loan term) and \"TD013\" (total count of P2P lending queries) implies that the impact of loan term on loan default risk is influenced by the level of P2P lending queries. This suggests that the effect of loan term on default risk may vary associated with the borrower's query behavior related to P2P lending.\n",
    "- As \"AP004\" (loan term) is a discrete numeric variable, the plot showcases the risk pattern across specific loan term categories. Each distinct category of loan term represents a different group, and their impact on default risk may not follow a linear relationship.\n",
    "- This insight allows lenders to make informed decisions about loan terms and effectively manage the risk associated with different loan durations and borrower behaviors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e6a4988f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"AP003_bin_WOE\", shap_values, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521d9aa2",
   "metadata": {},
   "source": [
    "- The aobve plot shows an approximately positive trend between \"AP003_bin_WOE\" (WOE-transformed code representing the level of education of the borrower) and the target variable, suggesting that certain categories of the WOE-transformed \"CODE_EDUCATION\" variable are associated with a higher probability of loan default. This indicates that specific educational backgrounds may influence the risk of loan default, and some categories may exhibit a higher default propensity. However, since the variable dictionary of this dataset does not provide definition of the categories for \"CODE_EDUCATION\" variable, we cannot tell the meaning of categories that associated with higher default risk. \n",
    "- The plot indicates frequent interactions between \"AP003_bin_WOE\" (WOE-transformed \"CODE_EDUCATION\") and \"TD013\" (total count of P2P lending queries). This suggests that the impact of \"AP003_bin_WOE\" (WOE-transformed \"CODE_EDUCATION\") on loan default risk is influenced by the level of P2P lending queries. It implies that the effect of education-related WOE on default risk may vary associated with the borrower's query behavior related to P2P lending.\n",
    "- As \"AP003_bin_WOE\" is a discrete numeric variable derived from the WOE transformation of the \"CODE_EDUCATION\" feature, the plot showcases the risk pattern across specific educational categories. Each distinct category represents a different group, and their impact on default risk may not follow a linear relationship.\n",
    "- This knowledge enables lenders to make informed decisions about lending to borrowers with different educational backgrounds and query behaviors, thus effectively managing loan default risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "38d2d29b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"PA028_bin_WOE\", shap_values, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb957d54",
   "metadata": {},
   "source": [
    "- The above plot shows an approximately positive trend between \"PA028_bin_WOE\" (WOE-transformed average length of collection or high-risk calls made by the borrower) and the target variable, suggesting that certain categories of the WOE-transformed \"AVG_LEN_COLLECTION_OR_HIGH_RISK_CALLS\" variable are associated with a higher probability of loan default. This indicates that borrowers with certain average lengths of collection or high-risk calls may be at a higher risk of loan default.\n",
    "- The plot indicates frequent interactions between \"PA028_bin_WOE\" (WOE-transformed \"AVG_LEN_COLLECTION_OR_HIGH_RISK_CALLS\") and \"TD013\" (total count of P2P lending queries). This suggests that the impact of \"PA028_bin_WOE\" on loan default risk is influenced by the level of P2P lending queries. It implies that the effect of the average length of collection or high-risk calls on default risk may vary associated with the borrower's query behavior related to P2P lending. \n",
    "- As \"PA028_bin_WOE\" is a discrete numeric variable derived from the WOE transformation of the \"AVG_LEN_COLLECTION_OR_HIGH_RISK_CALLS\" feature, the plot showcases the risk pattern across specific average length categories. Each distinct category represents a different group, and their impact on default risk may not follow a linear relationship. \n",
    "- This knowledge enables lenders to make informed decisions about lending to borrowers with different average lengths of collection or high-risk calls and query behaviors, thus effectively managing loan default risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b9869fad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"CR015\", shap_values, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56fdc3a4",
   "metadata": {},
   "source": [
    "- The plot shows an approximately negative trend between \"CR015\" (the maximum mobile credit card usage amount in a month) and the target variable, indicating that higher values of the maximum mobile credit card usage amount in a month are associated with a lower probability of loan default. This suggests that borrowers with higher credit card usage may have better creditworthiness and are less likely to default on their loans.\n",
    "- The plot indicates frequent interactions between \"CR015\" (the maximum mobile credit card usage amount in a month) and \"TD013\" (total count of P2P lending queries). This suggests that the impact of \"CR015\" (the maximum mobile credit card usage amount in a month) on loan default risk is influenced by the level of P2P lending queries. It implies that the effect of the maximum credit card usage on default risk may vary associated with the borrower's query behavior related to P2P lending.\n",
    "- As \"CR015\" is a discrete numeric variable, the plot showcases the risk pattern across specific credit card usage amount categories. Each distinct category represents a different group, and their impact on default risk may not follow a linear relationship. \n",
    "- Lenders can utilize this knowledge to assess the creditworthiness of borrowers based on their credit card usage and query behavior related to P2P lending, allowing them to make more informed lending decisions and effectively manage loan default risk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9034622",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### 3.3 Local Interpretability - Individual SHAP Value Plot <a class=\"anchor\" id=\"33\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cb2cdd",
   "metadata": {},
   "source": [
    "Our machine learning model produces the prediction for a record. To make sense of the prediction, the explainability for any individual observation is the most critical step to convince our audience to adopt our model. I am going to illustrate the interpretations with several examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "983f49eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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    "I will arbitrarily select some observations from X_test and show them in the following table."
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       "    .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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TD013</th>\n",
       "      <th>AP004</th>\n",
       "      <th>AP003_bin_WOE</th>\n",
       "      <th>CR015</th>\n",
       "      <th>MB007_WOE</th>\n",
       "      <th>TD009</th>\n",
       "      <th>PA028_bin_WOE</th>\n",
       "      <th>MB005_bin_WOE</th>\n",
       "      <th>CD123_bin_WOE</th>\n",
       "      <th>TD005</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50885</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62544</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53407</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64690</th>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28417</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>3</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47377</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.07</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>4</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20537</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.07</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>3</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TD013  AP004  AP003_bin_WOE  CR015  MB007_WOE  TD009  PA028_bin_WOE  \\\n",
       "50885      2     12          -0.51      6      -0.04      2           0.34   \n",
       "62544      7     12           0.07      5      -0.04      4           0.36   \n",
       "53407      4     12           0.07      2      -0.04      4           0.02   \n",
       "64690     13     12           0.07      3      -0.08     12          -0.15   \n",
       "28417      4     12           0.07      3      -0.04      4          -0.15   \n",
       "47377      9      9           0.07      4      -0.08      4          -0.15   \n",
       "20537      4      6           0.07      6      -0.15      4          -0.30   \n",
       "\n",
       "       MB005_bin_WOE  CD123_bin_WOE  TD005  predict  \n",
       "50885           0.16          -0.37      2     0.12  \n",
       "62544          -0.23          -0.16      2     0.27  \n",
       "53407           0.22           0.05      3     0.19  \n",
       "64690          -0.12           0.12      6     0.28  \n",
       "28417          -0.12          -0.37      3     0.13  \n",
       "47377           0.22          -0.02      4     0.27  \n",
       "20537          -0.13          -0.11      3     0.17  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the predictions and put them with the test data.\n",
    "X_output = X_test.copy()\n",
    "X_output.loc[:,'predict'] = np.round(model.predict(X_output),2)\n",
    "\n",
    "# Randomly pick some observations\n",
    "random_picks = np.arange(1,330,50)\n",
    "S = X_output.iloc[random_picks]\n",
    "S "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9707684b",
   "metadata": {},
   "source": [
    "Then I will write a function to prepare for the individual SHAP value plot production. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0a2a49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write in a function to produce the SHAP values with force_plot \n",
    "# for individual observations in the above table\n",
    "def shap_plot(j):\n",
    "    explainerModel = shap.TreeExplainer(model) \n",
    "    shap_values_Model = explainerModel.shap_values(S) \n",
    "    p = shap.force_plot(explainerModel.expected_value, shap_values_Model[j], S.iloc[[j]])\n",
    "    return(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "47ee6271",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TD013             6.799797\n",
       "AP004            11.229141\n",
       "AP003_bin_WOE    -0.012985\n",
       "CR015             4.927453\n",
       "MB007_WOE        -0.002967\n",
       "TD009             5.406469\n",
       "PA028_bin_WOE    -0.011354\n",
       "MB005_bin_WOE    -0.009388\n",
       "CD123_bin_WOE    -0.005251\n",
       "TD005             3.595828\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the average values of the predictors\n",
    "X_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3d610c54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1934375"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the base value\n",
    "# the mean prediction \n",
    "# the value that would be predicted if we did not know any features for the current output\n",
    "Y_test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3533178",
   "metadata": {},
   "source": [
    "Here is the interpretation of the individual observations' SHAP value plots:\n",
    "- The output value is the prediction for that observation.\n",
    "- The base value: The original paper explains that the base value E(y_hat) is “the value that would be predicted if we did not know any features for the current output.” In other words, it is the mean prediction of Y_test, 0.19.\n",
    "- Red/blue: Features that push the prediction higher (to the right) are shown in red, and those pushing the prediction lower are in blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2b5230d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.19367812499999998, \"outValue\": 0.2733716795585997, \"link\": \"identity\", \"featureNames\": [\"TD013\", \"AP004\", \"AP003_bin_WOE\", \"CR015\", \"MB007_WOE\", \"TD009\", \"PA028_bin_WOE\", \"MB005_bin_WOE\", \"CD123_bin_WOE\", \"TD005\", \"predict\"], \"features\": {\"0\": {\"effect\": 0.03165479116662226, \"value\": 7.0}, \"1\": {\"effect\": 0.016342254134544296, \"value\": 12.0}, \"2\": {\"effect\": 0.00784781364248586, \"value\": 0.07}, \"3\": {\"effect\": 0.011414102613577077, \"value\": 5.0}, \"4\": {\"effect\": -0.0043582369008716764, \"value\": -0.04}, \"5\": {\"effect\": -0.001175820823821681, \"value\": 4.0}, \"6\": {\"effect\": 0.037717031168208556, \"value\": 0.36}, \"7\": {\"effect\": -0.004030439094735314, \"value\": -0.23}, \"8\": {\"effect\": -0.01335697547863347, \"value\": -0.16}, \"9\": {\"effect\": -0.0023609658687761695, \"value\": 2.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Individual SHAP Value Plot for Observation 1 of S\n",
    "shap_plot(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a05d07c",
   "metadata": {},
   "source": [
    "- The output value, which is the prediction for this variable is 0.27. It is larger than the base value (0.19), meaning its possibility to default is higher than the average.\n",
    "- \"PA028_bin_WOE\" (WOE-transformed average length of collection or high-risk calls made by the borrower) has a positive impact on the target variable (loan default). The value of \"PA028_bin_WOE\" (0.36) is higher than the average (-0.01), so it pushes the prediction to the right. \n",
    "- \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) has a positive impact on the target variable (loan default).  The value of \"TD013\" (7) is higher than the average (6.8), so it pushes the prediction to the right. \n",
    "- \"AP004\" (the term or duration of the loan in months) has a positive impact on the target variable (loan default). The value of \"AP004\" (12) is higher than the average (11.23), so it pushes the prediction to the right. \n",
    "- \"CD123_bin_WOE\" (count of distinct outbound calls made by the borrower in the last 3 months) has a negative impact on the target variable (loan default). The value of \"CD123_bin_WOE\" (-0.16) is lower than the average (-0.005), so it pushes the prediction to the left. \n",
    "- \"MB007_WOE\" (WOE-transformed brand or manufacturer of the borrower's mobile device) has a negative impact on the target variable (loan default). The value of \"MB007_WOE\" (-0.04) is lower than the average (-0.003), so it pushes the prediction to the left. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20b85dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.19367812499999998, \"outValue\": 0.18962920683097864, \"link\": \"identity\", \"featureNames\": [\"TD013\", \"AP004\", \"AP003_bin_WOE\", \"CR015\", \"MB007_WOE\", \"TD009\", \"PA028_bin_WOE\", \"MB005_bin_WOE\", \"CD123_bin_WOE\", \"TD005\", \"predict\"], \"features\": {\"0\": {\"effect\": -0.0459882274348823, \"value\": 4.0}, \"1\": {\"effect\": 0.012040176859516884, \"value\": 12.0}, \"2\": {\"effect\": 0.007792832357391078, \"value\": 0.07}, \"3\": {\"effect\": 0.015936806637179814, \"value\": 2.0}, \"4\": {\"effect\": -0.003520779409501163, \"value\": -0.04}, \"5\": {\"effect\": 0.0034526302572568785, \"value\": 4.0}, \"6\": {\"effect\": -0.0023307971960216415, \"value\": 0.02}, \"7\": {\"effect\": 0.004911500358633511, \"value\": 0.22}, \"8\": {\"effect\": 0.004994413621961619, \"value\": 0.05}, \"9\": {\"effect\": -0.0013374742205560092, \"value\": 3.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
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   ],
   "source": [
    "shap_plot(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0dc50a",
   "metadata": {},
   "source": [
    "- The output value, which is the prediction for this variable is 0.19. It is lower than the base value (0.1937), meaning its possibility to default is lower than the average.\n",
    "- \"CR015\" (the maximum mobile credit card usage amount in a month) has a positive impact on the target variable (loan default). \"CR015\" is negatively correlated to the target variable, and the value of \"CR015\" (2) is lower than the average (4.93), so it pushes the prediction to the right. \n",
    "- \"AP004\" (the term or duration of the loan in months) has a positive impact on the target variable (loan default). The value of \"AP004\" (12) is higher than the average (11.23), so it pushes the prediction to the right. \n",
    "- \"AP003_bin_WOE\" (WOE-transformed code representing the level of education of the borrower) has a positive impact on the target variable (loan default).  The value of \"AP003_bin_WOE\" (0.07) is higher than the average (-0.01), so it pushes the prediction to the right. \n",
    "- \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) has a negative impact on the target variable (loan default). The value of \"TD013\" (4) is lower than the average (6.8), so it pushes the prediction to the left. \n",
    "- \"MB007_WOE\" (WOE-transformed brand or manufacturer of the borrower's mobile device) has a negative impact on the target variable (loan default). The value of \"MB007_WOE\" (-0.04) is lower than the average (-0.003), so it pushes the prediction to the left. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1fcb949a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='i2BC9KXA81IGY8KQ6WU66'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.19367812499999998, \"outValue\": 0.2816148099527217, \"link\": \"identity\", \"featureNames\": [\"TD013\", \"AP004\", \"AP003_bin_WOE\", \"CR015\", \"MB007_WOE\", \"TD009\", \"PA028_bin_WOE\", \"MB005_bin_WOE\", \"CD123_bin_WOE\", \"TD005\", \"predict\"], \"features\": {\"0\": {\"effect\": 0.05418866776436227, \"value\": 13.0}, \"1\": {\"effect\": 0.016362154074185398, \"value\": 12.0}, \"2\": {\"effect\": 0.015214372505454826, \"value\": 0.07}, \"3\": {\"effect\": 0.007963838818467378, \"value\": 3.0}, \"4\": {\"effect\": -0.0027098677761596595, \"value\": -0.08}, \"5\": {\"effect\": 0.012721060322763775, \"value\": 12.0}, \"6\": {\"effect\": -0.009785919565716972, \"value\": -0.15}, \"7\": {\"effect\": -0.011753607198608164, \"value\": -0.12}, \"8\": {\"effect\": 0.007979054850631235, \"value\": 0.12}, \"9\": {\"effect\": -0.0022430688426583146, \"value\": 6.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
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     },
     "execution_count": 50,
     "metadata": {},
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    }
   ],
   "source": [
    "shap_plot(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833c3a76",
   "metadata": {},
   "source": [
    "- The output value, which is the prediction for this variable is 0.28. It is higher than the base value (0.19), meaning its possibility to default is higher than the average.\n",
    "- \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) has a positive impact on the target variable (loan default). The value of \"TD013\" (13) is higher than the average (6.8), so it pushes the prediction to the right. \n",
    "- \"AP004\" (the term or duration of the loan in months) has a positive impact on the target variable (loan default). The value of \"AP004\" (12) is higher than the average (11.23), so it pushes the prediction to the right. \n",
    "- \"AP003_bin_WOE\" (WOE-transformed code representing the level of education of the borrower) has a positive impact on the target variable (loan default). The value of \"AP003_bin_WOE\" (0.07) is higher than the average (-0.01), so it pushes the prediction to the right. \n",
    "- \"MB005_bin_WOE\" (WOE-transformed number of years the borrower's phone has been active) has a negative impact on the target variable (loan default). The value of \"MB005_bin_WOE\" (-0.12) is lower than the average (-0.009), so it pushes the prediction to the left.\n",
    "- \"PA028_bin_WOE\" (WOE-transformed average length of collection or high-risk calls made by the borrower) has a negative impact on the target variable (loan default). The value of \"PA028_bin_WOE\" (-0.15) is lower than the average (-0.01), so it pushes the prediction to the left. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "74165463",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
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       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_plot(4)"
   ]
  },
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    "- The output value, which is the prediction for this variable is 0.13. It is lower than the base value (0.19), meaning its possibility to default is lower than the average.\n",
    "- \"CR015\" (the maximum mobile credit card usage amount in a month) has a positive impact on the target variable (loan default). \"CR015\" is negatively correlated to the target variable, and the value of \"CR015\" (3) is lower than the average (4.93), so it pushes the prediction to the right. \n",
    "- \"AP004\" (the term or duration of the loan in months) has a positive impact on the target variable (loan default). The value of \"AP004\" (12) is higher than the average (11.23), so it pushes the prediction to the right. \n",
    "- \"AP003_bin_WOE\" (WOE-transformed code representing the level of education of the borrower) has a positive impact on the target variable (loan default).  The value of \"AP003_bin_WOE\" (0.07) is higher than the average (-0.01), so it pushes the prediction to the right. \n",
    "- \"TD013\" (total count of queries related to peer-to-peer (P2P) lending in the last 6 months) has a negative impact on the target variable (loan default). The value of \"TD013\" (4) is lower than the average (6.8), so it pushes the prediction to the left. \n",
    "- \"CD123_bin_WOE\" (count of distinct outbound calls made by the borrower in the last 3 months) has a negative impact on the target variable (loan default). The value of \"CD123_bin_WOE\" (-0.37) is lower than the average (-0.005), so it pushes the prediction to the left. \n",
    "- \"PA028_bin_WOE\" (WOE-transformed average length of collection or high-risk calls made by the borrower) has a negative impact on the target variable (loan default). The value of \"PA028_bin_WOE\" (-0.15) is lower than the average (-0.01), so it pushes the prediction to the left. \n",
    "- \"MB007_WOE\" (WOE-transformed brand or manufacturer of the borrower's mobile device) has a negative impact on the target variable (loan default). The value of \"MB007_WOE\" (-0.04) is lower than the average (-0.003), so it pushes the prediction to the left. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f55404",
   "metadata": {},
   "source": [
    "## 4. Conclusion <a class=\"anchor\" id=\"4\"></a>"
   ]
  },
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    "In this report I have demostrated how the SHAP values help to explain the predictions of the machine learning model for loan prediction project. \n",
    "- Global Interpretability:\n",
    "    - The variable importance plot lists the most significant variables in descending order, and the positive and negative relationships of the predictors with the target variable.\n",
    "    - SHAP dependence plot for each predictor shows its marginal effect on the predicted outcome of the machine learning model. It tells whether the relationship between the target and a feature is linear, monotonic or more complex. \n",
    "- Local Interpretability: The individual SHAP value plot shows that how to make sense of the prediction for each individual observation. It shows the association between each feature and the predicted outcome. \n",
    "- As a result, this information can be valuable in the loan default prediction project, as it helps identify important features that contribute to the model's decision-making process. Lenders or financial institutions can use this insight to better assess the creditworthiness of borrowers and make more informed lending decisions. They might consider implementing different risk mitigation strategies or adjusting loan terms based on the observed interactions between these variables."
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