Final_Project_Code_Group5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Default Prediction\n",
    "\n",
    "Final Project of Machine Learning I (DATS 6202 - 11, Spring 2018)  \n",
    "**Authors:** Liwei Zhu,Wenye Ouyang,Xiaochi Li (Group 5)   \n",
    "Data Science @ The George Washington University  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Source: https://www.kaggle.com/wendykan/lending-club-loan-data/data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Abstract:** Loan default is always the threat to any financial institution and should be predicted in advance based on various features of the applicant. This study aims at applying machine models, including decision tree, logistic regression and random forest to classify applicants with and without loan default from a group of predicting variables, and evaluate their performance. Comparison between using unbalanced training set and balanced training set suggests that balancing the data is the key to improve model performance. The study also found that regression is the best model to classify those applicants with loan default, and the recall score can be 70% with balanced data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment specification\n",
    "+ This notebook should be run under python with all necessary Data Science packages, Anaconda environment is recommended.\n",
    "+ Training the model will occupy a lot of memory (about 3 GB or 2915MB), please turn off other programs if necessary.\n",
    "+ Please use `conda install -c glemaitre imbalanced-learn` in conda prompt to install SMOTE package, or follow instructions on http://contrib.scikit-learn.org/imbalanced-learn/stable/install.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan = pd.read_csv(\"D:\\loan.csv\",low_memory=False) #read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175    1313524     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863    1277178    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358    1311748     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade     ...      total_bal_il il_util  \\\n",
       "0     10.65       162.87     B        B2     ...               NaN     NaN   \n",
       "1     15.27        59.83     C        C4     ...               NaN     NaN   \n",
       "2     15.96        84.33     C        C5     ...               NaN     NaN   \n",
       "3     13.49       339.31     C        C1     ...               NaN     NaN   \n",
       "4     12.69        67.79     B        B5     ...               NaN     NaN   \n",
       "\n",
       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "\n",
       "  total_cu_tl inq_last_12m  \n",
       "0         NaN          NaN  \n",
       "1         NaN          NaN  \n",
       "2         NaN          NaN  \n",
       "3         NaN          NaN  \n",
       "4         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887379 entries, 0 to 887378\n",
      "Data columns (total 74 columns):\n",
      "id                             887379 non-null int64\n",
      "member_id                      887379 non-null int64\n",
      "loan_amnt                      887379 non-null float64\n",
      "funded_amnt                    887379 non-null float64\n",
      "funded_amnt_inv                887379 non-null float64\n",
      "term                           887379 non-null object\n",
      "int_rate                       887379 non-null float64\n",
      "installment                    887379 non-null float64\n",
      "grade                          887379 non-null object\n",
      "sub_grade                      887379 non-null object\n",
      "emp_title                      835917 non-null object\n",
      "emp_length                     842554 non-null object\n",
      "home_ownership                 887379 non-null object\n",
      "annual_inc                     887375 non-null float64\n",
      "verification_status            887379 non-null object\n",
      "issue_d                        887379 non-null object\n",
      "loan_status                    887379 non-null object\n",
      "pymnt_plan                     887379 non-null object\n",
      "url                            887379 non-null object\n",
      "desc                           126028 non-null object\n",
      "purpose                        887379 non-null object\n",
      "title                          887227 non-null object\n",
      "zip_code                       887379 non-null object\n",
      "addr_state                     887379 non-null object\n",
      "dti                            887379 non-null float64\n",
      "delinq_2yrs                    887350 non-null float64\n",
      "earliest_cr_line               887350 non-null object\n",
      "inq_last_6mths                 887350 non-null float64\n",
      "mths_since_last_delinq         433067 non-null float64\n",
      "mths_since_last_record         137053 non-null float64\n",
      "open_acc                       887350 non-null float64\n",
      "pub_rec                        887350 non-null float64\n",
      "revol_bal                      887379 non-null float64\n",
      "revol_util                     886877 non-null float64\n",
      "total_acc                      887350 non-null float64\n",
      "initial_list_status            887379 non-null object\n",
      "out_prncp                      887379 non-null float64\n",
      "out_prncp_inv                  887379 non-null float64\n",
      "total_pymnt                    887379 non-null float64\n",
      "total_pymnt_inv                887379 non-null float64\n",
      "total_rec_prncp                887379 non-null float64\n",
      "total_rec_int                  887379 non-null float64\n",
      "total_rec_late_fee             887379 non-null float64\n",
      "recoveries                     887379 non-null float64\n",
      "collection_recovery_fee        887379 non-null float64\n",
      "last_pymnt_d                   869720 non-null object\n",
      "last_pymnt_amnt                887379 non-null float64\n",
      "next_pymnt_d                   634408 non-null object\n",
      "last_credit_pull_d             887326 non-null object\n",
      "collections_12_mths_ex_med     887234 non-null float64\n",
      "mths_since_last_major_derog    221703 non-null float64\n",
      "policy_code                    887379 non-null float64\n",
      "application_type               887379 non-null object\n",
      "annual_inc_joint               511 non-null float64\n",
      "dti_joint                      509 non-null float64\n",
      "verification_status_joint      511 non-null object\n",
      "acc_now_delinq                 887350 non-null float64\n",
      "tot_coll_amt                   817103 non-null float64\n",
      "tot_cur_bal                    817103 non-null float64\n",
      "open_acc_6m                    21372 non-null float64\n",
      "open_il_6m                     21372 non-null float64\n",
      "open_il_12m                    21372 non-null float64\n",
      "open_il_24m                    21372 non-null float64\n",
      "mths_since_rcnt_il             20810 non-null float64\n",
      "total_bal_il                   21372 non-null float64\n",
      "il_util                        18617 non-null float64\n",
      "open_rv_12m                    21372 non-null float64\n",
      "open_rv_24m                    21372 non-null float64\n",
      "max_bal_bc                     21372 non-null float64\n",
      "all_util                       21372 non-null float64\n",
      "total_rev_hi_lim               817103 non-null float64\n",
      "inq_fi                         21372 non-null float64\n",
      "total_cu_tl                    21372 non-null float64\n",
      "inq_last_12m                   21372 non-null float64\n",
      "dtypes: float64(49), int64(2), object(23)\n",
      "memory usage: 501.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_loan.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns/rows that has too many NA\n",
    "+ Remove columns whose NA is more than 70%\n",
    "+ Remove rows whose NA is more than 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dti_joint                      0.999426\n",
       "verification_status_joint      0.999424\n",
       "annual_inc_joint               0.999424\n",
       "il_util                        0.979020\n",
       "mths_since_rcnt_il             0.976549\n",
       "all_util                       0.975916\n",
       "max_bal_bc                     0.975916\n",
       "open_rv_24m                    0.975916\n",
       "open_rv_12m                    0.975916\n",
       "total_cu_tl                    0.975916\n",
       "total_bal_il                   0.975916\n",
       "open_il_24m                    0.975916\n",
       "open_il_12m                    0.975916\n",
       "open_il_6m                     0.975916\n",
       "open_acc_6m                    0.975916\n",
       "inq_fi                         0.975916\n",
       "inq_last_12m                   0.975916\n",
       "desc                           0.857977\n",
       "mths_since_last_record         0.845553\n",
       "mths_since_last_major_derog    0.750160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_rate = df_loan.isnull().sum(axis = 0).sort_values(ascending = False)/float((len(df_loan)))\n",
    "null_rate[null_rate > 0.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan.drop(null_rate[null_rate>0.7].index,axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan.dropna(axis = 0,thresh=30,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out the columns that has too few/ too much unique data\n",
    "Inspect these columns to decide whether to remove them or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "unique_rate = df_loan.apply(lambda x: len(pd.unique(x)),axis = 0).sort_values(ascending = False) #unique rate and sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            887379\n",
       "url                           887379\n",
       "member_id                     887379\n",
       "total_pymnt                   506726\n",
       "total_pymnt_inv               506616\n",
       "tot_cur_bal                   327343\n",
       "total_rec_int                 324635\n",
       "emp_title                     299272\n",
       "out_prncp_inv                 266244\n",
       "total_rec_prncp               260227\n",
       "out_prncp                     248332\n",
       "last_pymnt_amnt               232451\n",
       "revol_bal                      73740\n",
       "installment                    68711\n",
       "title                          63145\n",
       "annual_inc                     49385\n",
       "recoveries                     23055\n",
       "total_rev_hi_lim               21252\n",
       "collection_recovery_fee        20708\n",
       "tot_coll_amt                   10326\n",
       "funded_amnt_inv                 9856\n",
       "total_rec_late_fee              6181\n",
       "dti                             4086\n",
       "funded_amnt                     1372\n",
       "loan_amnt                       1372\n",
       "revol_util                      1357\n",
       "zip_code                         935\n",
       "earliest_cr_line                 698\n",
       "int_rate                         542\n",
       "mths_since_last_delinq           156\n",
       "total_acc                        136\n",
       "last_credit_pull_d               104\n",
       "issue_d                          103\n",
       "next_pymnt_d                     101\n",
       "last_pymnt_d                      99\n",
       "open_acc                          78\n",
       "addr_state                        51\n",
       "sub_grade                         35\n",
       "pub_rec                           33\n",
       "delinq_2yrs                       30\n",
       "inq_last_6mths                    29\n",
       "purpose                           14\n",
       "collections_12_mths_ex_med        13\n",
       "emp_length                        12\n",
       "loan_status                       10\n",
       "acc_now_delinq                     9\n",
       "grade                              7\n",
       "home_ownership                     6\n",
       "verification_status                3\n",
       "application_type                   2\n",
       "term                               2\n",
       "initial_list_status                2\n",
       "pymnt_plan                         2\n",
       "policy_code                        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n    887369\n",
      "y        10\n",
      "Name: pymnt_plan, dtype: int64 \n",
      " n    0.999989\n",
      "y    0.000011\n",
      "Name: pymnt_plan, dtype: float64\n",
      "f    456848\n",
      "w    430531\n",
      "Name: initial_list_status, dtype: int64 \n",
      " f    0.514829\n",
      "w    0.485171\n",
      "Name: initial_list_status, dtype: float64\n",
      "INDIVIDUAL    886868\n",
      "JOINT            511\n",
      "Name: application_type, dtype: int64 \n",
      " INDIVIDUAL    0.999424\n",
      "JOINT         0.000576\n",
      "Name: application_type, dtype: float64\n",
      "0.0     883236\n",
      "1.0       3866\n",
      "2.0        208\n",
      "3.0         28\n",
      "4.0          7\n",
      "5.0          3\n",
      "6.0          1\n",
      "14.0         1\n",
      "Name: acc_now_delinq, dtype: int64 \n",
      " 0.0     0.995331\n",
      "1.0     0.004357\n",
      "2.0     0.000234\n",
      "3.0     0.000032\n",
      "4.0     0.000008\n",
      "5.0     0.000003\n",
      "6.0     0.000001\n",
      "14.0    0.000001\n",
      "Name: acc_now_delinq, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def column_analyse(x,df = df_loan): #print count for columns that only has few uniques\n",
    "    print(df[x].value_counts(),\"\\n\",df[x].value_counts()/len(df[x]))\n",
    "\n",
    "column_analyse(\"pymnt_plan\")\n",
    "column_analyse(\"initial_list_status\")\n",
    "column_analyse(\"application_type\")\n",
    "column_analyse(\"acc_now_delinq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect result:\n",
    "According to the unique table:\n",
    "\n",
    "+ We need to remove id,url,member_id because these three are 100% unique   \n",
    "+ policy_code only has one value   \n",
    "+ And payment_plan,application_type,acc_now_delinq are highly unbalanced (more than 90% are single value)  \n",
    "+ emp_title,zip_code,title are too detail for analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "delete_cols = [\"policy_code\", \"pymnt_plan\", \"url\", \"id\", \"member_id\", \"application_type\", \"acc_now_delinq\", \"emp_title\",\n",
    "               \"zip_code\", \"title\"]\n",
    "df_loan.drop(delete_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer the format of some columns\n",
    "Transfer the value to number for further analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan[\"term\"] = df_loan[\"term\"].str.split(\" \").str[1] # transform term to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df_loan[\"emp_length\"] = df_loan[\"emp_length\"].str.extract(\"(\\d+)\").astype(float)\n",
    "df_loan[\"emp_length\"] = df_loan[\"emp_length\"].fillna(df_loan.emp_length.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform datetimes to a period\n",
    "col_dates = df_loan.dtypes[df_loan.dtypes == \"datetime64[ns]\"].index\n",
    "for d in col_dates:\n",
    "    df_loan[d] = df_loan[d].dt.to_period[\"M\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform some colums that are highly biased\n",
    "More that half of the rows in these columns are in one value,so we tranform them into categorical variable.\n",
    "\n",
    "+ delinq_2yrs: 80% are 0\n",
    "+ inq_last_6mths:56% are 0\n",
    "+ pub_rec:84% are 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0     716961\n",
      "1.0     113224\n",
      "2.0      33551\n",
      "3.0      11977\n",
      "4.0       5327\n",
      "5.0       2711\n",
      "6.0       1471\n",
      "7.0        784\n",
      "8.0        461\n",
      "9.0        284\n",
      "10.0       192\n",
      "11.0       121\n",
      "12.0        89\n",
      "13.0        64\n",
      "14.0        45\n",
      "15.0        28\n",
      "16.0        17\n",
      "18.0        11\n",
      "17.0        10\n",
      "19.0         8\n",
      "22.0         3\n",
      "21.0         2\n",
      "26.0         2\n",
      "20.0         2\n",
      "30.0         1\n",
      "39.0         1\n",
      "27.0         1\n",
      "29.0         1\n",
      "24.0         1\n",
      "Name: delinq_2yrs, dtype: int64 \n",
      " 0.0     0.807954\n",
      "1.0     0.127594\n",
      "2.0     0.037809\n",
      "3.0     0.013497\n",
      "4.0     0.006003\n",
      "5.0     0.003055\n",
      "6.0     0.001658\n",
      "7.0     0.000884\n",
      "8.0     0.000520\n",
      "9.0     0.000320\n",
      "10.0    0.000216\n",
      "11.0    0.000136\n",
      "12.0    0.000100\n",
      "13.0    0.000072\n",
      "14.0    0.000051\n",
      "15.0    0.000032\n",
      "16.0    0.000019\n",
      "18.0    0.000012\n",
      "17.0    0.000011\n",
      "19.0    0.000009\n",
      "22.0    0.000003\n",
      "21.0    0.000002\n",
      "26.0    0.000002\n",
      "20.0    0.000002\n",
      "30.0    0.000001\n",
      "39.0    0.000001\n",
      "27.0    0.000001\n",
      "29.0    0.000001\n",
      "24.0    0.000001\n",
      "Name: delinq_2yrs, dtype: float64\n",
      "0.0     497905\n",
      "1.0     241494\n",
      "2.0      94117\n",
      "3.0      37398\n",
      "4.0      10758\n",
      "5.0       3985\n",
      "6.0       1231\n",
      "7.0        195\n",
      "8.0        122\n",
      "9.0         50\n",
      "10.0        24\n",
      "11.0        15\n",
      "12.0        15\n",
      "15.0         9\n",
      "14.0         6\n",
      "13.0         6\n",
      "18.0         4\n",
      "16.0         3\n",
      "17.0         2\n",
      "19.0         2\n",
      "24.0         2\n",
      "31.0         1\n",
      "32.0         1\n",
      "25.0         1\n",
      "28.0         1\n",
      "20.0         1\n",
      "33.0         1\n",
      "27.0         1\n",
      "Name: inq_last_6mths, dtype: int64 \n",
      " 0.0     0.561096\n",
      "1.0     0.272143\n",
      "2.0     0.106062\n",
      "3.0     0.042144\n",
      "4.0     0.012123\n",
      "5.0     0.004491\n",
      "6.0     0.001387\n",
      "7.0     0.000220\n",
      "8.0     0.000137\n",
      "9.0     0.000056\n",
      "10.0    0.000027\n",
      "11.0    0.000017\n",
      "12.0    0.000017\n",
      "15.0    0.000010\n",
      "14.0    0.000007\n",
      "13.0    0.000007\n",
      "18.0    0.000005\n",
      "16.0    0.000003\n",
      "17.0    0.000002\n",
      "19.0    0.000002\n",
      "24.0    0.000002\n",
      "31.0    0.000001\n",
      "32.0    0.000001\n",
      "25.0    0.000001\n",
      "28.0    0.000001\n",
      "20.0    0.000001\n",
      "33.0    0.000001\n",
      "27.0    0.000001\n",
      "Name: inq_last_6mths, dtype: float64\n",
      "0.0     751572\n",
      "1.0     113266\n",
      "2.0      14854\n",
      "3.0       4487\n",
      "4.0       1564\n",
      "5.0        757\n",
      "6.0        385\n",
      "7.0        170\n",
      "8.0        113\n",
      "9.0         50\n",
      "10.0        42\n",
      "11.0        23\n",
      "12.0        16\n",
      "13.0        12\n",
      "15.0         6\n",
      "18.0         5\n",
      "16.0         5\n",
      "21.0         4\n",
      "17.0         3\n",
      "14.0         2\n",
      "49.0         2\n",
      "19.0         2\n",
      "40.0         1\n",
      "86.0         1\n",
      "20.0         1\n",
      "23.0         1\n",
      "63.0         1\n",
      "22.0         1\n",
      "28.0         1\n",
      "34.0         1\n",
      "26.0         1\n",
      "54.0         1\n",
      "Name: pub_rec, dtype: int64 \n",
      " 0.0     0.846957\n",
      "1.0     0.127641\n",
      "2.0     0.016739\n",
      "3.0     0.005056\n",
      "4.0     0.001762\n",
      "5.0     0.000853\n",
      "6.0     0.000434\n",
      "7.0     0.000192\n",
      "8.0     0.000127\n",
      "9.0     0.000056\n",
      "10.0    0.000047\n",
      "11.0    0.000026\n",
      "12.0    0.000018\n",
      "13.0    0.000014\n",
      "15.0    0.000007\n",
      "18.0    0.000006\n",
      "16.0    0.000006\n",
      "21.0    0.000005\n",
      "17.0    0.000003\n",
      "14.0    0.000002\n",
      "49.0    0.000002\n",
      "19.0    0.000002\n",
      "40.0    0.000001\n",
      "86.0    0.000001\n",
      "20.0    0.000001\n",
      "23.0    0.000001\n",
      "63.0    0.000001\n",
      "22.0    0.000001\n",
      "28.0    0.000001\n",
      "34.0    0.000001\n",
      "26.0    0.000001\n",
      "54.0    0.000001\n",
      "Name: pub_rec, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_analyse(\"delinq_2yrs\")\n",
    "column_analyse(\"inq_last_6mths\")\n",
    "column_analyse(\"pub_rec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 80% are 0\n",
    "df_loan[\"delinq_2yrs_cat\"] = 0\n",
    "df_loan.loc[df_loan[\"delinq_2yrs\"] > 0, \"delinq_2yrs_cat\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 56% are 0\n",
    "df_loan[\"inq_last_6mths_cat\"] = 0\n",
    "df_loan.loc[df_loan[\"inq_last_6mths\"]>0 ,\"inq_last_6mths_cat\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 84% are 0\n",
    "df_loan[\"pub_rec_cat\"] = 0\n",
    "df_loan.loc[df_loan[\"pub_rec\"]>0,\"pub_rec_cat\"] = 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make some new columns based on calcution of old variables\n",
    "Funded_amnt\tis the total amount committed to that loan at that point in time.  \n",
    "Funded_amnt_inv\tis total amount committed by investors for that loan at that point in time.  \n",
    "When they are not same(the investor pays fewer than expected), mark it as less.  \n",
    "Open_acc is the number of open credit lines in the borrower's credit file.  \n",
    "total_acc is the total number of credit lines currently in the borrower's credit file  \n",
    "The ratio means how many loans does the applicant have now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan['acc_ratio'] = df_loan.open_acc / df_loan.total_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loan['amt_difference'] = 'eq'\n",
    "df_loan.loc[(df_loan['funded_amnt'] - df_loan['funded_amnt_inv']) > 0,'amt_difference'] = 'less'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the features we select:  \n",
    "Reference Data Dictionary :**LCDataDictionary.xlsx**\n",
    "\n",
    "|Feature|Defination|\n",
    "|----|----|\n",
    "|loan_amnt|The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.|\n",
    "|amt_difference|Whether investor pays fewer than requested by the borrower|\n",
    "|term|The number of payments on the loan. Values are in months and can be either 36 or 60.|\n",
    "|installment|Number of currently active installment trades|\n",
    "|grade|LC assigned loan grade|\n",
    "|emp_length|Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 |\n",
    "|home_ownership|The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.|\n",
    "|annual_inc|The self-reported annual income provided by the borrower during registration.|\n",
    "|verification_status|Indicates if the co-borrowers' joint income was verified by LC, not verified, or if the income source was verified|\n",
    "|purpose|A category provided by the borrower for the loan request. |\n",
    "|dti|A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.|\n",
    "|delinq_2yrs_cat|The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years|\n",
    "|inq_last_6mths_cat|The number of inquiries in past 6 months (excluding auto and mortgage inquiries)|\n",
    "|open_acc|The number of open credit lines in the borrower's credit file.|\n",
    "|pub_rec|Number of derogatory public records|\n",
    "|pub_rec_cat|Categorical transformation of pub_rec|\n",
    "|acc_ratio|How many loans accounts(ratio to total account) does the applicant have now| \n",
    "|initial_list_status|The initial listing status of the loan.|\n",
    "|loan_status|Current status of the loan|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['loan_amnt', 'amt_difference', 'term', \n",
    "            'installment', 'grade','emp_length',\n",
    "            'home_ownership', 'annual_inc','verification_status',\n",
    "            'purpose', 'dti', 'delinq_2yrs_cat', 'inq_last_6mths_cat', \n",
    "            'open_acc', 'pub_rec', 'pub_rec_cat', 'acc_ratio', 'initial_list_status',  \n",
    "            'loan_status'\n",
    "           ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform loan_status\n",
    "+ Current, which means the loan status may still change in the future, is't useful to us\n",
    "+ Make \"Charged Off\" to 0, all the others to 1\n",
    "+ If there are still NA in the row, drop the row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "Current                                                67.815330\n",
       "Fully Paid                                             23.408600\n",
       "Charged Off                                             5.099061\n",
       "Late (31-120 days)                                      1.306206\n",
       "Issued                                                  0.953369\n",
       "In Grace Period                                         0.704659\n",
       "Late (16-30 days)                                       0.265614\n",
       "Does not meet the credit policy. Status:Fully Paid      0.224031\n",
       "Default                                                 0.137371\n",
       "Does not meet the credit policy. Status:Charged Off     0.085758\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.groupby(\"loan_status\").size().sort_values(ascending=False)/len(df_loan)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_clean = df_loan.loc[df_loan.loan_status != \"Current\",features] #remove the record where status is current "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (X_clean.loan_status == 'Charged Off')\n",
    "X_clean['target'] = 0\n",
    "X_clean.loc[mask,'target'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot encoding on categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features = ['term','amt_difference', 'grade', 'home_ownership', 'verification_status', 'purpose', 'delinq_2yrs_cat', \n",
    "                'inq_last_6mths_cat', 'pub_rec_cat', 'initial_list_status',]\n",
    "X_clean.dropna(axis=0, how = 'any', inplace = True) #drop all records that contains NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X_clean[X_clean.columns[:-2]], columns=cat_features).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Steps of preprocessing\n",
    "+ Define target as y\n",
    "+ Train test split\n",
    "+ Standardize X_train with fit_transform, X_test with tranform only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X_clean['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_raw, X_test_raw, y_train, y_test = train_test_split(X, y.values, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "stdsc = StandardScaler()\n",
    "X_train = stdsc.fit_transform(X_train_raw)\n",
    "X_test = stdsc.transform(X_test_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of preprocess \n",
    "By the end of this step, we have X_train,X_test,y_train,y_test for further steps.\n",
    "\n",
    "## Begin model train/test/evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the classifiers from Scikt-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model sets \n",
    "We will compare the models based on the model sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "different_model_comparison = {\n",
    "    \"Random Forest\":RandomForestClassifier(random_state=0,n_estimators=10),\n",
    "    \"Logistic Regression\":LogisticRegression(random_state=0),\n",
    "    \"Decision Tree\":DecisionTreeClassifier(random_state=0)#,\n",
    "    #\"SVM\":SVC(random_state=0,probability=True) #too slow\n",
    "}\n",
    "\n",
    "# We tried to use 1~60 ,but it will be slow and the improvemnet is not significant.\n",
    "different_tree_number_comaprison = {\n",
    "    \"Random Forest(1)\":RandomForestClassifier(random_state=0,n_estimators=1),\n",
    "    \"Random Forest(5)\":RandomForestClassifier(random_state=0,n_estimators=5),\n",
    "    \"Random Forest(10)\":RandomForestClassifier(random_state=0,n_estimators=10),\n",
    "    \"Random Forest(15)\":RandomForestClassifier(random_state=0,n_estimators=15),\n",
    "    \"Random Forest(20)\":RandomForestClassifier(random_state=0,n_estimators=20),\n",
    "    \"Random Forest(25)\":RandomForestClassifier(random_state=0,n_estimators=25),\n",
    "    \"Random Forest(30)\":RandomForestClassifier(random_state=0,n_estimators=30)\n",
    "}"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the general purpose functions\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to train model, return a dictionary of trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrapping this function so we can easily change from original data to balanced data\n",
    "# function to train model, return a dictionary of trained model\n",
    "def train_model(model_dict,X_train,y_train):\n",
    "    for model in model_dict:\n",
    "        print(\"Training:\",model)\n",
    "        model_dict[model].fit(X_train,y_train)\n",
    "    return model_dict"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to evaluate model performance  \n",
    "We use the following metrics: Precision, Recall,F1 score,AUC\n",
    "\n",
    "![Confusin Matrix](attachment:image.png)\n",
    "\n",
    "|Metric|Definition|Meaning|\n",
    "|----|----|----|\n",
    "|Precision|TP/(TP+FP)|The ability of the classifier not to label  a sample that is negative as positive|\n",
    "|Recall|TP/(TP+FN)|The ability of the classifier to find all the positive samples|\n",
    "|F1 Score|F1 = 2 \\* (precision \\* recall) / (precision + recall)|Weighted average of the precision and recall|\n",
    "|roc_auc_score|area under the curve(AUC) |Determine which of the used models predicts the classes best|\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrapping this function so we can easily change the model and evaluate them\n",
    "# function to evaluate model performance \n",
    "from sklearn import metrics\n",
    "def model_eval(clf_name,clf,X_test,y_test):\n",
    "    print(\"Evaluating:\",clf_name)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    y_score = clf.predict_proba(X_test)[:,1]\n",
    "    confusion_matrix = pd.crosstab(y_test, y_pred, rownames=['True'], colnames= ['Predicted'], margins=False)\n",
    "    report = pd.Series({\n",
    "        \"model\":clf_name,\n",
    "        \"precision\":metrics.precision_score(y_test, y_pred),\n",
    "        \"recall\":metrics.recall_score(y_test, y_pred),\n",
    "        \"f1\":metrics.f1_score(y_test, y_pred),\n",
    "        'roc_auc_score' : metrics.roc_auc_score(y_test, y_score)\n",
    "    })\n",
    "    # draw ROC \n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_score, drop_intermediate = False, pos_label = 1)\n",
    "    plt.figure(1, figsize=(6,6))\n",
    "    plt.xlabel('false positive rate')\n",
    "    plt.ylabel('true positive rate')\n",
    "    plt.plot(fpr, tpr,label=clf_name)\n",
    "    plt.plot([0,1],[0,1], color = 'black')\n",
    "    plt.legend()\n",
    "    return report,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Higher level function\n",
    "The function that calls *train_model* and *model_eval*, this makes model training and evaluation much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_eval_model(model_dict,X_train,y_train,X_test,y_test):\n",
    "    cols = ['model', 'roc_auc_score', 'precision', 'recall','f1']\n",
    "    model_report = pd.DataFrame(columns = cols)\n",
    "    cm_dict = {}\n",
    "    model_dict = train_model(model_dict,X_train,y_train)\n",
    "    for model in model_dict:\n",
    "        report,confusion_matrix = model_eval(model,model_dict[model],X_test,y_test)\n",
    "        model_report = model_report.append(report,ignore_index=True)\n",
    "        cm_dict[model] = confusion_matrix\n",
    "    return model_report,cm_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_which_bar(df,col_name):\n",
    "    df.set_index(\"model\").loc[:,col_name].plot(kind='bar', stacked=True, sort_columns=True, figsize = (16,10))\n",
    "    plt.title(col_name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Evaulate model sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion about business problem and solution\n",
    "\n",
    "`1- sum(y_train)/len(y_train)` = 0.8422603414724436  \n",
    "In our data, about 84% of the applicants will pay the loan on time.  \n",
    "Which means, even we approve all the applicants' rquest, 84% will be paid on time.  \n",
    "However, the bad loans can be more harmful to financial institution.  \n",
    "In this situation, False Negative means fail to identify a bad loan.  \n",
    "So we should try to improve **Recall** to find more bad loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run models using unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Random Forest\n",
      "Training: Logistic Regression\n",
      "Training: Decision Tree\n",
      "Evaluating: Random Forest\n",
      "Evaluating: Logistic Regression\n",
      "Evaluating: Decision Tree\n"
     ]
    },
    {
     "data": {
      "image/png": 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3IjIvAuDnVJ/JLd8xyXn+9JxiNhyI4ayytHxDh6Z1GPD0/ZVvuBtV0lWS5s1B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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_report,cm_dict = train_eval_model(different_model_comparison,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Decision Tree': Predicted      0      1\n",
       " True                   \n",
       " 0          59975  11981\n",
       " 1          10528   3188, 'Logistic Regression': Predicted      0   1\n",
       " True                \n",
       " 0          71940  16\n",
       " 1          13693  23, 'Random Forest': Predicted      0     1\n",
       " True                  \n",
       " 0          70902  1054\n",
       " 1          13220   496}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run models using balanced Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to install this package, paste into conda Prompt  \n",
    "`conda install -c glemaitre imbalanced-learn`"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We balance the classes using the SMOTE ( Synthetic Minority Over-sampling Technique).  \n",
    "**The minority class is oversampled by SMOTE** \n",
    "\n",
    "http://contrib.scikit-learn.org/imbalanced-learn/stable/generated/imblearn.over_sampling.SMOTE.html  \n",
    "![USAGE](attachment:image.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "index_split = int(len(X)*0.7) #30% testing\n",
    "\n",
    "X_train_bal, y_train_bal = SMOTE(random_state=0).fit_sample(X_train,y_train)\n",
    "X_test_bal, y_test_bal = X_test, y_test\n",
    "\n",
    "# X_train_bal, y_train_bal = SMOTE(random_state=0).fit_sample(X_train[0:index_split, :], y[0:index_split])\n",
    "# X_test_bal, y_test_bal = X_train[index_split:], y[index_split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "336734"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199899"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train_bal)/len(y_train_bal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before balancing the data, only 15% are bad loan. Which will harm the performance of the model  \n",
    "After balancing the data using SMOTE, 50% are bad loan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15773965852755642"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train)/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train_bal)/len(y_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Random Forest\n",
      "Training: Logistic Regression\n",
      "Training: Decision Tree\n",
      "Evaluating: Random Forest\n",
      "Evaluating: Logistic Regression\n",
      "Evaluating: Decision Tree\n"
     ]
    },
    {
     "data": {
      "image/png": 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HAWjo0oCedbrhbm2crrH56iI2HArn38BYZMBLzWvS+3kfLMzKfprS5uQQO38u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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_report_bal,cm_dict_bal = train_eval_model(different_model_comparison,X_train_bal,y_train_bal,X_test_bal,y_test_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Decision Tree': Predicted      0      1\n",
       " True                   \n",
       " 0          57748  14208\n",
       " 1          10165   3551, 'Logistic Regression': Predicted      0      1\n",
       " True                   \n",
       " 0          43863  28093\n",
       " 1           4847   8869, 'Random Forest': Predicted      0     1\n",
       " True                  \n",
       " 0          64855  7101\n",
       " 1          11160  2556}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_dict_bal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Random Forest(1)\n",
      "Training: Random Forest(5)\n",
      "Training: Random Forest(10)\n",
      "Training: Random Forest(15)\n",
      "Training: Random Forest(20)\n",
      "Training: Random Forest(25)\n",
      "Training: Random Forest(30)\n",
      "Evaluating: Random Forest(1)\n",
      "Evaluating: Random Forest(5)\n",
      "Evaluating: Random Forest(10)\n",
      "Evaluating: Random Forest(15)\n",
      "Evaluating: Random Forest(20)\n",
      "Evaluating: Random Forest(25)\n",
      "Evaluating: Random Forest(30)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DKhcZ/jHe2Nup0Sol9JefIsR0EqlMiVXeh+3bVNTV2vD113LnSAOKtBNkv/4J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\ngg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_report_n_trees,cm_dict_n_trees = train_eval_model(different_tree_number_comaprison,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: Random Forest(1)\n",
      "Training: Random Forest(5)\n",
      "Training: Random Forest(10)\n",
      "Training: Random Forest(15)\n",
      "Training: Random Forest(20)\n",
      "Training: Random Forest(25)\n",
      "Training: Random Forest(30)\n",
      "Evaluating: Random Forest(1)\n",
      "Evaluating: Random Forest(5)\n",
      "Evaluating: Random Forest(10)\n",
      "Evaluating: Random Forest(15)\n",
      "Evaluating: Random Forest(20)\n",
      "Evaluating: Random Forest(25)\n",
      "Evaluating: Random Forest(30)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WOfiDKADNKjg4hOnTZ7Fo0X+ZN28+//zny8TFxdjTQEtKii/5OTc3Nx57bAELFszD1dUN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\nqaxIXk9hXRFOCkf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AdcBInU73KyAB5up0uueBVL1ev0Gn090DHNbpdBbgALC9BdsiCG1a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XaN3O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\nFAAhhPBSUgCEEMJLSQEQQggv9f+x8oGEiNg3MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_report_n_trees_bal,cm_dict_n_trees_bal = train_eval_model(different_tree_number_comaprison,X_train_bal,y_train_bal,X_test_bal,y_test_bal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the best parameter for Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest with different tree numbers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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>model</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest(1)</td>\n",
       "      <td>0.528303</td>\n",
       "      <td>0.205771</td>\n",
       "      <td>0.214202</td>\n",
       "      <td>0.209902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest(5)</td>\n",
       "      <td>0.580098</td>\n",
       "      <td>0.258442</td>\n",
       "      <td>0.099883</td>\n",
       "      <td>0.144082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest(10)</td>\n",
       "      <td>0.606063</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.036162</td>\n",
       "      <td>0.064981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest(15)</td>\n",
       "      <td>0.620836</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>0.072396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest(20)</td>\n",
       "      <td>0.628378</td>\n",
       "      <td>0.319043</td>\n",
       "      <td>0.023330</td>\n",
       "      <td>0.043481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest(25)</td>\n",
       "      <td>0.634525</td>\n",
       "      <td>0.330164</td>\n",
       "      <td>0.027851</td>\n",
       "      <td>0.051368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest(30)</td>\n",
       "      <td>0.638832</td>\n",
       "      <td>0.364247</td>\n",
       "      <td>0.019758</td>\n",
       "      <td>0.037483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  roc_auc_score  precision    recall        f1\n",
       "0   Random Forest(1)       0.528303   0.205771  0.214202  0.209902\n",
       "1   Random Forest(5)       0.580098   0.258442  0.099883  0.144082\n",
       "2  Random Forest(10)       0.606063   0.320000  0.036162  0.064981\n",
       "3  Random Forest(15)       0.620836   0.314815  0.040901  0.072396\n",
       "4  Random Forest(20)       0.628378   0.319043  0.023330  0.043481\n",
       "5  Random Forest(25)       0.634525   0.330164  0.027851  0.051368\n",
       "6  Random Forest(30)       0.638832   0.364247  0.019758  0.037483"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report_n_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SVfdO8k1JPrDCcwEAADAzqxxZvSrJuVV1XZJtSS6sqkuz+PzpWw/xmJcmeV1VfVsWR1if\n3N2fWz7unVlE8pXd/Vef/0sAAABgqzlsrHb3nUkuOeDuGw+y31ds+HpPkm87yD5vS/K2I14lAAAA\ns7LKacAAAACwqcQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoA\nAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAA\nAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAA\nDEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAw\nHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBw\nxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMJy1w+1QVduTXJ7ktCS3\nJ7mou3cdsM96kuuSnNrdt1XViUlel+SEJPdOcml3f6CqviPJy5L8xfKhL+zu3ztqrwYAAIAt4bCx\nmuRxSXZ091lVdWaSy5JcsH9jVT06yUuT3G/DYy5N8p7ufkVVVZJfT3L68tePdvebjtYLAAAAYOtZ\nJVbPTnJ1knT39VV1xgHb70zyyCQf3nDfy7M4Crv/e9y2/PohSR5cVc9K8qEkP9bd+/6BawcAAGCL\nWiVWT0hy84bbd1TV2v7I7O53J8niAOpCd9+0vO/kLE4HftZy07uT/FaSP0vyi0kuSfILh/rGJ510\n36ytHbfqa+EoW1/fOfUSZs8M5svs583858vs583858vsD26VWL0lycbfve2rHA2tqlOTvCHJczZ8\nLvXKDSH7liTfeU/PsWfPrSssj2Nl9+69Uy9h1tbXd5rBTJn9vJn/fJn9vJn/fM199vcU6qtcDfja\nJOclyfIzqzcc7gFV9bVJfjPJk7r7Hcv7tiX5o6r6Z8vdviV3P3UYAAAAkqx2ZPWqJOdW1XVJtiW5\nsKouTbKru996iMe8JMmOJK9cnh58c3dfUFUXJXlzVX0myR8nueLzfgUAAABsOYeN1e6+M4vPlm50\n40H2+4oNX19w4Pbl/e9K8q4jWyIAAABzs8ppwAAAALCpxCoAAADDEasAAAAMR6wCAAAwHLEKAADA\ncMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADD\nEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxH\nrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByx\nCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQq\nAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasA\nAAAMR6wCAAAwHLEKAADAcNYOt0NVbU9yeZLTktye5KLu3nXAPutJrktyanffVlX3SfK6JF+aZG+S\nH+ju3VV1fpIXJNmX5MruvuKovhoAAAC2hFWOrD4uyY7uPivJc5NctnFjVT06ybuS3G/D3U9PckN3\nn5PkV5P8RFXdK8nLkzwqycOSPK2qTv78XwIAAABbzSqxenaSq5Oku69PcsYB2+9M8sgkf3ewxyR5\nx3L7KUl2dfee7v5skmuSnPMPXzoAAABb1WFPA05yQpKbN9y+o6rWuntfknT3u5Okqg71mL1JTjzI\n8+y//5BOOum+WVs7boUlciysr++cegmT+q7fePrUS5jMG5/w6qmXMHtzf//NnfnPl9nPm/nPl9kf\n3CqxekuSjb972/eH6oqP2ZnkpoM8z/77D2nPnltXWB7Hyu7de6deAhMx+2mtr+80gxkz//ky+3kz\n//ma++zvKdRXOQ342iTnJUlVnZnkhiN5TJJvTfL+JB9L8oCq+pKquneSb0rygRWeCwAAgJlZ5cjq\nVUnOrarrkmxLcmFVXZrF50/feojHvDrJa6rqmiSfTfKk7v7c8nHvzCKSr+zuv/r8XwIAAABbzWFj\ntbvvTHLJAXffeJD9vmLD17cmefxB9nlbkrcd8SoBAACYlVVOAwYAAIBNJVYBAAAYjlgFAABgOGIV\nAACA4YhVAAAAhiNWAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUA\nAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAAhiNWAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYjVgEA\nABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAAhiNWAQAAGI5YBQAA\nYDhiFQAAgOGIVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA\n4YhVAAAAhiNWAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACG\nI1YBAAAYjlgFAABgOGIVAACA4awdboeq2p7k8iSnJbk9yUXdvWvD9qcmuTjJviQv7u63V9Urknzd\ncpeTk9zU3WdW1auSfGOSvcttF3T3zUft1QAAALAlHDZWkzwuyY7uPquqzkxyWZILkqSqTk7yw0nO\nSLIjyTVV9e7uftZy+72SXJPkqcvnOj3Jo7v7U0f3ZQAAALCVrHIa8NlJrk6S7r4+izDd76FJru3u\n25dHSHcledCG7c9M8q7uvmF5hPYBSX65qq6tqqcclVcAAADAlrPKkdUTkmw8VfeOqlrr7n0H2bY3\nyYlJUlX3zuL04Icut31Rkp9P8v8kOS7J+6rqD7r7jw71jU866b5ZWztu1dfCUba+vnPqJTARs5+e\nGcyb+c+X2c+b+c+X2R/cKrF6S5KNv3vbl6F6sG07k9y0/PqRSX5/w2dSb03yyu6+NUmq6r1ZfA72\nkLG6Z8+tKyyPY2X37r2H34ktyeyntb6+0wxmzPzny+znzfzna+6zv6dQX+U04GuTnJcky8+s3rBh\n24eSnFNVO6rqxCSnJPnoctsjk7xjw75fncVnWo9bfpb17CR/uOqLAAAAYD5WidWrktxWVdcleXmS\nZ1fVpVX17d39N0leleT9Sd6b5Me7+7bl4yrJn+5/ku7+WJLXJ7k+ye8l+dXu/i9H76UAAACwVRz2\nNODuvjPJJQfcfeOG7VckueIgj/u2g9z3s0l+9siXCQAAwJyscmQVAAAANpVYBQAAYDhiFQAAgOGI\nVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAAhiNW\nAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgF\nAABgOGIVAACA4YhVAAAAhiNWAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYjVgEAABiOWAUAAGA4YhUA\nAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAAhiNWAQAAGI5YBQAAYDhiFQAAgOGIVQAA\nAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAAhiNWAQAA\nGI5YBQAAYDhiFQAAgOGIVQAAAIazdrgdqmp7ksuTnJbk9iQXdfeuDdufmuTiJPuSvLi7315VX5Lk\n40k+utztqu5+5cH2PaqvBgAAgC3hsLGa5HFJdnT3WVV1ZpLLklyQJFV1cpIfTnJGkh1Jrqmqdyc5\nPcmvd/cz9z/Jofbt7tuP5gsCAADgC98qsXp2kquTpLuvr6ozNmx7aJJrl8F5e1XtSvKgJA9JcnpV\n/V6ST2YRqV9/iH3/01F7NQAAAGwJq3xm9YQkN2+4fUdVrR1i294kJya5MckLu/thSX4ryc/fw74A\nAABwN6scWb0lyc4Nt7d3975DbNuZ5KYkH0xy6/K+q5L82yS/eoh9D+mkk+6btbXjVlgix8L6+s7D\n78SWZPbTM4N5M//5Mvt5M//5MvuDWyVWr01yfpI3Lj+zesOGbR9K8tNVtSPJ8UlOyeKiSq9J8qYk\nb0zyLUk+fA/7HtKePbfe02aOsd279069BCZi9tNaX99pBjNm/vNl9vNm/vM199nfU6ivEqtXJTm3\nqq5Lsi3JhVV1aZJd3f3WqnpVkvdncUrxj3f3bVX13CRXVtUzknw6iysI/83B9v28XhkAAABb0mFj\ntbvvTHLJAXffuGH7FUmuOOAxf5bkmw/yXP/LvgAAAHCgVS6wBAAAAJtKrAIAADAcsQoAAMBwxCoA\nAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAA\nAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwnLWpFwAAI3n1S3936iVM5unPffjUSwCA\n/8mRVQAAAIYjVgEAABiOWAUAAGA4YhUAAIDhiFUAAACGI1YBAAAYjlgFAABgOGIVAACA4YhVAAAA\nhiNWAQAAGM7a1AsAGM3HL3rytN9/0u+efPW//5WJVwAA4MgqAAAAAxKrAAAADEesAgAAMByxCgAA\nwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAA\nwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADCctakXAAAAU3v+f/qTqZcwqZ/5\n+gdMvQT4XziyCgAAwHDEKgAAAMMRqwAAAAznsJ9ZrartSS5PclqS25Nc1N27Nmx/apKLk+xL8uLu\nfntVfXmSK5fPvy3J07q7q+rSJD+YZPfy4Rd3dx/NFwQAAMAXvlUusPS4JDu6+6yqOjPJZUkuSJKq\nOjnJDyc5I8mOJNdU1buT/FSSX+ju36qqRyd5SZLvSHJ6ku/v7g8f/ZcCAADAVrHKacBnJ7k6Sbr7\n+izCdL+HJrm2u2/v7puT7EryoCQ/kuS3l/usJblt+fVDkjyvqq6pqucdhfUDAACwBa1yZPWEJDdv\nuH1HVa11976DbNub5MTu/lSSVFUl+bksjs4myRuS/L9JbklyVVU9trvffqhvfNJJ983a2nErvxiO\nrvX1nVMvgYnMffYfn3oBE5v7/OfM7KdnBkzFv3vT8vt/cKvE6i1JNv7ubV+G6sG27UxyU5JU1Tdn\n8VnX71t+XnVbklcsj8Cmqn47yYOTHDJW9+y5ddXXwTGwe/feqZfARMx+3sx/vsx+WuvrO82Ayfh3\nbzpzf+/fU6ivEqvXJjk/yRuXn1m9YcO2DyX56arakeT4JKck+egyVF+Z5DHd/efLfU9YbjslyaeT\nPCKLizABAADA3awSq1clObeqrsviyr4XLq/qu6u731pVr0ry/iw+//rj3X1bVb0iyb2TvGZxJnC6\nuy+uqucneV8WVxV+T3f/zjF4TQAAAHyBO2ysdvedSS454O4bN2y/IskVBzzmtEM812uTvPbIlwkA\ncGz994/822m//6TfPfnyB79g4hUA3N0qVwMGAACATSVWAQAAGI5YBQAAYDhiFQAAgOGIVQAAAIYj\nVgEAABiOWAUAAGA4YhUAAIDhrE29AAAAgKk85aXvnXoJk7ryuY+YegmH5MgqAAAAwxGrAAAADEes\nAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEK\nAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAAAAxHrAIAADAcsQoAAMBwxCoA\nAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAAMByxCgAAwHDEKgAAAMMRqwAA\nAAxHrAIAADAcsQoAAMBwxCoAAADDEasAAAAMR6wCAAAwHLEKAADAcMQqAAAAwxGrAAAADEesAgAA\nMByxCgAAwHDEKgAAAMMRqwB63PEDAAAUV0lEQVQAAAxHrAIAADCctcPtUFXbk1ye5LQktye5qLt3\nbdj+1CQXJ9mX5MXd/faq+t+S/FqS+yT5RJILu/vWg+17tF8QAAAAX/hWObL6uCQ7uvusJM9Nctn+\nDVV1cpIfTvKNSR6d5CVVdXySFyT5te4+J8lHklx8D/sCAADA3awSq2cnuTpJuvv6JGds2PbQJNd2\n9+3dfXOSXUketPExSd6R5JH3sC8AAADczSqxekKSmzfcvqOq1g6xbW+SEw+4/2D3bbwfAAAA7uaw\nn1lNckuSnRtub+/ufYfYtjPJTRvu/8xB7jtw30NaX9+5bYX1HTNvu+yCKb89E3vjE1499RKYyPpb\n3jT1EpjQCy47f+olMJH1R71s6iUwoSvOO33qJTARf+Yf1ypHVq9Ncl6SVNWZSW7YsO1DSc6pqh1V\ndWKSU5J8dONjknxrkvffw74AAABwN9vuuuuue9xhw9WAH5RkW5ILswjRXd391uUVfp+WRfj+THe/\nqarul+Q1WRw9/VSSJ3X3pw+27zF6XQAAAHwBO2ysAgAAwGZb5TRgAAAA2FRiFQAAgOGIVQAAAIaz\nyo+uATZJVT0wyT9O8snu/tjU6wEAgKm4wNJAquqkJN+YZawkuaa79067Ko61qjo+yY8l+a4kf5vk\nb5KclOTLkvxGkpd392emWyHHWlX98yT/KsnDk3xJFu//9yT5pe7+8wmXxjFm9vNm/vNl9vNm/qsT\nqwOoqvUkL03yNUk6yV9nESsPzuLn2v6b7v7b6VbIsVRVv5Lk9Une0913brh/W5LHJPnu7v7+iZbH\nMVZVL0hy/yS/meSP8v+//78hyROy+DFhPznZAjlmzH7ezH++zH7ezP/IOA14DC9M8u+6++MHbqiq\nU5K8IMkPbfqq2BTd/eRD3H9Xkncsf7F1vbm7P3rAfZ9M8rYkb6uqUydYE5vD7OfN/OfL7OfN/I+A\nI6swsap6Znf/fFWdnOTnk3xdkg8n+b8dUZ+HqvrSJOckOTHJTUk+0N1/Pe2qONaq6gHd/SfLr8/L\n4myaD3f31dOujM3ivT9P3vt476/O1YBhev9y+c9XJrkqyYOyOC3430+2IjZNVV2U5O1ZfF79/0hy\ndhZ/s3rJpAtjM/xSklTVc5M8I8nfJfnBqnrhpKtiU3jvz5r3/ox57x8ZpwEPoKpekuSgh7i7+/mb\nvBymc7/u/rXl12+rqmdPuho2y4VJvrG7P7f/jqq6d5Jrk/ziZKtiM31bkm/u7n1V9YtJfi/JiyZe\nE8ee9z7e+/PkvX8ExOoY/jbJ05P8dJJtE6+FzXdqVb0yyVpVPSLJ7yb5zmmXxCa6V5L7JPnchvvu\nm0P8BRZbypdW1YOzuLjGCVkcXblPkh2TrorN4r0/X9778+a9fwTE6gC6+xVV9ZAkn+ju/zj1eth0\nX53k9CR/leSLsvgP1ncmecqUi2LT/FSSD1fVnyS5OYs/uHxVkksnXRWb4T9kMecHJvmh5V9afTTJ\n8yZdFZvlUO/9H5l0VWwG7/158//9I+ACS4Ooqh1JdnT3TVOvhc1VVf+0uz/xD93OF76qWktyShb/\nw7olyce6e9+0q2IKVXVCd98y9TrYHN777Oe9Py8HvPdvTnKj9/7BidUBiJV5q6pXZHEqyK8l+Wh3\nf275M1ZPT/J9SY7r7mdOuUYAjq6qekCSlyS5LcmLNlwd9tXd/fRJF8cxtZz9S5N8JslPdveu5f1m\nPwPLA1QXZ/ExwGuTvDbJHUme0d095dpG5DTgMfxoVd1jrCQRK1tUdz+rqr4hyXOSPKyqtmfxP7Br\nk1ze3ddPukCOqar6mUNtc4G1rc3sZ++Xs4jVeyX5rar63u7+SJKvmXZZbIKNs3+L2c/OryT5WJJT\nk7w4i3D9+yS/kOTc6ZY1JrE6ALFCd3+wqn6su/9y/31VVf6GbRY+GRdYmyuzn7nufleSVNWuJG+u\nqsfERVZmwexn7Z909xOXf96/obvfkyTL2xxArA5CrMxXVT0wyT9N8rNV9a+z+EPr9ixOEfq6KdfG\nsecCa/Nl9rO3r6rOT/I73d1V9a+y+NmL95p4XRx7Zj9vn6uq7+nu11fVaUlSVQ/P4s9+HMBvygCq\n6oFV9agkb6+qc6vqUcu/YfuNqdfGpjgpyXcnuV+SJy2/fnySy6dcFJvqqUn+YOpFMAmzn6+nZHHl\n9xOTpLvfl+RZST475aLYFGY/b9+T5Iwk2XBRpccnuWSyFQ3MBZYGUFXnZPEfrsckuXp5951JPtjd\nvzzZwthUVXV6d//h8uvt3X3n1Gvi2HOBtfky+3kz//ky+3kz/yPjNOABdPf7k7xfrMze/avqq5Mc\nn8UpwS/r7p+belEccy6wNl9mP2/mP19mP2/mfwQcWR1IVT0+i39Bj0/ys0nEyoxU1QeTnJfkDUke\nm+Rd3f2waVfFZlheYO2Hkzwsi49nuMDaTJj9vJn/fJn9vJn/6sTqQMTKvFXV7yf5l0mu6O7vqKo/\n7O7Tp14Xm6Oq/pkLrM2T2c+b+c+X2c+b+a/GacBjuX35z73dfXtV7Zx0NWy2P83iQivPrKoXJvng\nxOthE7ga9HyZ/byZ/3yZ/byZ/5ERq2MRKzPW3U+uqi/u7r+vqj/o7r+Zek1sigOvBp0sLrDmatBb\nn9nPm/nPl9nPm/kfAacBD2ZDrJwsVualqv7PJL+Y5B8leX0WH7p/+7SrYrO4wNp8mf28mf98mf28\nmf9q/JzVgSxj5R1VdUOSJ1fVY6deE5vqVUkuTPKpJP8hyU9Ouho22/2r6olV9QNJ/rqqnjP1gtg0\nZj9v5j9fZj9v5r8CsToWsTJz3b0ryV3dvTvJ3qnXw6Z6TpJ3J/neJF+e5Pxpl8MmMvt5M//5Mvt5\nM/8ViNXBiJVZ+7uqujjJF1XVE5PcNPWC2FR3u8BaEhdYmw+znzfzny+znzfzX4FYHYtYmbcfTPLP\nsziyfsbyNvOx/wJrV7rA2uyY/byZ/3yZ/byZ/wpcYGkgVXVCkucnOTXJx5L8THf/3bSrYrNU1eu7\n+3umXgfTcYG1+TL7eTP/+TL7eTP/wxOrAxEr81ZVb0ryoiQfz+IS5unuz066KDaNq0HPl9nPm/nP\nl9nPm/mvxmnAY9lRVQ+qqh1Vde+quvfUC2JTfXWSt2RxVL2T3DjtcthkLrA2X2Y/b+Y/X2Y/b+a/\nArE6FrEyY919apKvTPINSb6qu79y4iWxyVxgbb7Mft7Mf77Mft7M//DE6kDEyrxV1cOT/Nck70zy\nX6vq3GlXxCZzgbX5Mvt5M//5Mvt5M/8ViNWBiJXZe3GSs7v7wUm+cXmb+XA16Pky+3kz//ky+3kz\n/xWsTb0A7mZ/rHyiqr4syZuz+GHBzMMd3f2JJOnuv6qq26ZeEJvq1S6wNltmP2/mP19mP2/mvwKx\nOhaxMm+3VNUzk/x+km9K4scWzcuOqnpQXA16jsx+3sx/vsx+3sx/BWJ1LGJl3r43yU8k+ekkf5zk\nKdMuh022/wJr+92VxWfY2frMft7Mf77Mft7MfwV+zupAqurELGLllCxi5SXdvWfaVXGsVdVj/Vwt\nkqSqtiVZT/I/uvuOqdfD5jH7eTP/+TL7eTP/w3OBpQFU1WOTpLtv7u5/3d2P7e4fFaqzcen+L6rq\nN6ZcCNNxgbX5Mvt5M//5Mvt5M//ViNUxiJV527bh6y+dbBVMzdWg58vs583858vs5838VyBWxyBW\n5u2uQ3zNvNztAmtJXGBtPsx+3sx/vsx+3sx/BS6wNAaxMm/3r6qfyeIvLfZ/nSTp7udPtyw2mQus\nzZfZz5v5z5fZz5v5r0CsjkGszNsLDvE18+Jq0PNl9vNm/vNl9vNm/itwNeABVNUPHGpbd79mM9cC\nbC5Xg54vs583858vs5838z8yPrM6gO5+zaF+Tb024JhzgbX5Mvt5M//5Mvt5M/8jIFYBpuUCa/Nl\n9vNm/vNl9vNm/kfAZ1ZhEFV1YpKHJdmx/77ufuN0K2KTuMDafJn9vJn/fJn9vJn/EfCZ1YGIlXmr\nqg8m+ViSPcu77uruS+/hIWwBVfXnSV6fxd+0Pmn5dRIXWNvqzH7ezH++zH7ezP/IOLI6lnflgFhJ\nIlbn4+bufvLUi2DTuRr0fJn9vJn/fJn9vJn/EXBkdSBV9a7uftTU62AaVfUjST6dxeXLkyTd/fvT\nrQgAAKbjyOpY3llVl0SszNU5SY7P4lTwZHFk3fwBAJglsToWsTJvX9zdj5x6EQAAMAKxOhaxMm8f\nraonJvlIlleH6+6PT7skNosLrM2X2c+b+c+X2c+b+a9GrI5FrMzbactf+92V5BETrYXN5wJr82X2\n82b+82X282b+KxCrYxErM9bd31xV/zjJ/ZP8aXd/auo1salcDXq+zH7ezH++zH7ezH8FrgY8GLEy\nX1X1+CQvzuJv2R6Y5Ce7+3XTrorN4mrQ82X282b+82X282b+q3FkdSAHxkpViZV5uTTJQ7r776tq\nZ5L3Jv9fe/cXsmdBxnH8uzk0hnoiVAODYsGVjcpsYifJW6H0h0ooErMDGblZ2GkIESEEq4OSgf0v\nfEdokUQQeBKFFXRQUQ5R7Cqnhv0b5qJats3R28H9jN6W8j4vuPu+2e/7geGzZ25c46uM632e5xr2\nz+GBtVy2z2b/XLbPZv8luKzOi8tKtn939zGA7v5HVR2feiCNygNruWyfzf65bJ/N/ktwWZ0Xl5Vs\nh6vqswxfVbsaODzxPBqXB9Zy2T6b/XPZPpv9l+CyOi8uK9n2APuAaxg+v3DbtONoZB5Yy2X7bPbP\nZfts9l+CB5ZmpKq2MSwrlzEsK1/t7mennUpnW1Vd/Xw/5gfts3hgLZfts9k/l+2z2X9jLqsz4LKS\nraq+uXi4Ezgf+AXweuBYd69MNZfG5TXoXLbPZv9cts9m/+X4NuB5+PDin/+3rAArE82kkXT3DQBV\ndR/wnu4+VVXnAfdNO5lG5oG1XLbPZv9cts9m/yVsnXoADcvKYmF5Ctjd3TcDVwEeWMqyY93jbcCL\npxpEk/ifA2v4/38S22ezfy7bZ7P/EnxldV5cVrJ9HXi4qh4CXg18cuJ5NC4PrOWyfTb757J9Nvsv\nwVdW5+X0svId4BDw6Ynn0Yi6+/PAlcBngDd197cnHknj2gM8xnAN+jBw87TjaES2z2b/XLbPZv8l\neGBpZqrqYuBVeBUsTlVdDuwFXnT6ue7eM91EGoMH1nLZPpv9c9k+m/03x7cBz8iZy0pVuaxkWQXu\nBJ6ceA6NywNruWyfzf65bJ/N/pvgsjovq7isJPtzd39t6iE0Lq9B57J9Nvvnsn02+2+Oy+q8uKxk\ne6KqbgMeANYAuvv7046kEXlgLZfts9k/l+2z2X8JLqvz4rKS7QKgFt9g+G/A/jm8Bp3L9tnsn8v2\n2ey/BA8szUhV3XXGU2t+ZjVXVe3o7j9NPYfG44G1XLbPZv9cts9m/425rM6Yy0qWqrod+AjDh+23\nA7/p7l3TTqWxeA06l+2z2T+X7bPZfzm+DXhGnmtZAVxWcrwduBS4A/gc8IVpx9HIVvHAWqpVbJ9s\nFfunWsX2yVax/4ZcVufFZSXb0919oqou6u5Hq2r71ANpVB5Yy2X7bPbPZfts9l+Cy+q8uKxk+31V\n7QH+WVX7gYunHkij8sBaLttns38u22ez/xJcVufFZSXbPuBlwL3ATcD1k06jsXkNOpfts9k/l+2z\n2X8JHliakarayrCsHGVYVn7Q3Y9MOpTOuqraBrwb+Gt337947qXAge52YQ3lgbVcts9m/1y2z2b/\n5+YrqzPwPMvKvcABfHUtwd3AKWBHVe0CHmf4u7cOTDqVRuWBtVy2z2b/XLbPZv/lbJ16AAHDsvJe\n4BNVdWtVvRM4tPimc9/O7r4ReBvDW4FvB97c3funHUsjO31g7W7gMuAP046jEdk+m/1z2T6b/Zfg\nsjoPLivZ/g7Q3ScZ/p+81rd/R3q6u08AF3X3owxfZVUG22ezfy7bZ7P/ElxW58FlRacd6e6jUw+h\nSXhgLZfts9k/l+2z2X8JfmZ1flxW8uyqqnuALeseA9DdH5huLI3Ma9C5bJ/N/rlsn83+S3BZnQeX\nlWzvX/f4S5NNoUl4YC2X7bPZP5fts9l/c1xW58FlJVh3/3jqGTQpr0Hnsn02++eyfTb7b4LL6gy4\nrEjRdnb37qo6H/glcILhwJqfWz/32T6b/XPZPpv9N8EDS5I0LQ+s5bJ9Nvvnsn02+2+Cy6okzYcH\n1nLZPpv9c9k+m/03sGVtbW3qGSQpVlUdAX7IcGDtLYvHgAfWznW2z2b/XLbPZv/N8TOrkjQtD6zl\nsn02++eyfTb7b4KvrEqSJEmSZsfPrEqSJEmSZsdlVZIkSZI0Oy6rkiTNVFWtVNWPNvh3VqvqpnEm\nkiRpPC6rkiRJkqTZ8RqwJEkvsKpaAT4OnAReAXwPOAZcx/DXFbwDuBL4FMMXjh8D9nX3kaq6FrgD\nOA78et2v+Urgi8AlwDPAR7v7gZF+S5Ikjc5XViVJOjuuAm4BdgO3Ak91927gwcXzXwau6+7XAj8F\n7qyqC4CDwPu6+w3Av9b9egeBj3X3FcBe4Fuj/U4kSZqAy6okSWfHQ939ZHc/A/yF//7F778D3gX8\nvLufWDz3FeCtwGuAP3b3I4vnDwJU1YUMr8TeVVWHgHuAC6vqklF+J5IkTcC3AUuSdHacPOP7p9Y9\nPvOLxVsY/kxeWzw+8+ecBxzv7stP/0BVXQocfWFGlSRpfnxlVZKk8f0MeGNVvXzx/b3A/QxvEX5J\nVb1u8fwNAN39N+C3VfVBgKq6BvjJqBNLkjQyl1VJksZ3hGFB/W5VPQysALd097MMC+o3qupXwPZ1\nP+dG4ENV9SCwH7i+u9fGHVuSpPFsWVvzzzlJkiRJ0rz4yqokSZIkaXZcViVJkiRJs+OyKkmSJEma\nHZdVSZIkSdLsuKxKkiRJkmbHZVWSJEmSNDsuq5IkSZKk2XFZlSRJkiTNzn8AkbZXAiHZbnEAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_which_bar(model_report_n_trees,\"recall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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>model</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest(1)</td>\n",
       "      <td>0.546420</td>\n",
       "      <td>0.204179</td>\n",
       "      <td>0.361184</td>\n",
       "      <td>0.260881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest(5)</td>\n",
       "      <td>0.593825</td>\n",
       "      <td>0.237485</td>\n",
       "      <td>0.274278</td>\n",
       "      <td>0.254559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest(10)</td>\n",
       "      <td>0.613564</td>\n",
       "      <td>0.264678</td>\n",
       "      <td>0.186352</td>\n",
       "      <td>0.218714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest(15)</td>\n",
       "      <td>0.622800</td>\n",
       "      <td>0.265581</td>\n",
       "      <td>0.227107</td>\n",
       "      <td>0.244842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest(20)</td>\n",
       "      <td>0.628204</td>\n",
       "      <td>0.274133</td>\n",
       "      <td>0.179717</td>\n",
       "      <td>0.217104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest(25)</td>\n",
       "      <td>0.631632</td>\n",
       "      <td>0.272336</td>\n",
       "      <td>0.207568</td>\n",
       "      <td>0.235581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest(30)</td>\n",
       "      <td>0.634935</td>\n",
       "      <td>0.282168</td>\n",
       "      <td>0.181467</td>\n",
       "      <td>0.220881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  roc_auc_score  precision    recall        f1\n",
       "0   Random Forest(1)       0.546420   0.204179  0.361184  0.260881\n",
       "1   Random Forest(5)       0.593825   0.237485  0.274278  0.254559\n",
       "2  Random Forest(10)       0.613564   0.264678  0.186352  0.218714\n",
       "3  Random Forest(15)       0.622800   0.265581  0.227107  0.244842\n",
       "4  Random Forest(20)       0.628204   0.274133  0.179717  0.217104\n",
       "5  Random Forest(25)       0.631632   0.272336  0.207568  0.235581\n",
       "6  Random Forest(30)       0.634935   0.282168  0.181467  0.220881"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report_n_trees_bal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AABijlAIAADBGKQUAAGCMUgoAAMCYo35OaVWdkuSFSc5MckWSJ3b3RXv2f2uSx+zefU13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\n1vCQJP8gyQ8k+b+SvHB2HLb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      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_which_bar(model_report_n_trees_bal,\"recall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Model sets using balanced / unbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Unbalanced Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "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>model</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.606063</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.036162</td>\n",
       "      <td>0.064981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.677632</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.003344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.532962</td>\n",
       "      <td>0.210165</td>\n",
       "      <td>0.232429</td>\n",
       "      <td>0.220737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  roc_auc_score  precision    recall        f1\n",
       "0        Random Forest       0.606063   0.320000  0.036162  0.064981\n",
       "1  Logistic Regression       0.677632   0.589744  0.001677  0.003344\n",
       "2        Decision Tree       0.532962   0.210165  0.232429  0.220737"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Balanced Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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>model</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.613564</td>\n",
       "      <td>0.264678</td>\n",
       "      <td>0.186352</td>\n",
       "      <td>0.218714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.677789</td>\n",
       "      <td>0.239949</td>\n",
       "      <td>0.646617</td>\n",
       "      <td>0.350014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.530720</td>\n",
       "      <td>0.199955</td>\n",
       "      <td>0.258895</td>\n",
       "      <td>0.225639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  roc_auc_score  precision    recall        f1\n",
       "0        Random Forest       0.613564   0.264678  0.186352  0.218714\n",
       "1  Logistic Regression       0.677789   0.239949  0.646617  0.350014\n",
       "2        Decision Tree       0.530720   0.199955  0.258895  0.225639"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report_bal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion \n",
    "\n",
    "This project focused on building different machine learning models and evaluating the performance of random forest, decision tree and logistic regression with unbalanced train data and balanced train data. We found that after oversampling the minority class by Synthetic Minority Over-Sampling Technique (SMOTE) in the training set, the recall score improves for every model, especially logistic regression. The recall score indicates that the logistic regression can classify 65% of the applicants that will default. However, we can’t find a random forest model that works better than logistic regression.  \n",
    "Perlich, Provost, Simonoff (2003) state that logistic regression is likely to perform best when the signal to noise ratio is low, which in technical terms means that if the AUC of the best model is below 0.8, logistic very clearly outperformed tree induction.  We think the complexity of our data, in other words low signal to noise ratio can be the reason why logistic regression performs best.   \n",
    "Besides the high recall score after using balanced data, logistic regression also has other benefits such as easy to interpret by viewing the parameters, that’s the reason we would recommend the financial institutions to use logistic regression to perform default prediction. \n"
   ]
  }
 ],
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