Prosper Loan Data -  Visualization.ipynb

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    "## Introduction\n",
    "This project is on a data set from Prosper, which is America’s first marketplace lending platform, with over $7 billion in funded loans. This data set contains 113,937 loans with 81 variables on each loan,  including loan amount, borrower rate (or interest rate), current loan  status, borrower income, borrower employment status, borrower credit  history, and the latest payment information.\n",
    "\n",
    "The main purpose of this project is to summarize the characteristics of  variables that can affect the loan status and to get some ideas about the  relationships among multiple variables using summary statistics and data  visualizations."
   ]
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   "source": [
    "# import required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113937, 81)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "loan_data = pd.read_csv('prosperLoanData.csv')\n",
    "loan_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingKey</th>\n",
       "      <th>ListingNumber</th>\n",
       "      <th>ListingCreationDate</th>\n",
       "      <th>CreditGrade</th>\n",
       "      <th>Term</th>\n",
       "      <th>LoanStatus</th>\n",
       "      <th>ClosedDate</th>\n",
       "      <th>BorrowerAPR</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>LenderYield</th>\n",
       "      <th>...</th>\n",
       "      <th>LP_ServiceFees</th>\n",
       "      <th>LP_CollectionFees</th>\n",
       "      <th>LP_GrossPrincipalLoss</th>\n",
       "      <th>LP_NetPrincipalLoss</th>\n",
       "      <th>LP_NonPrincipalRecoverypayments</th>\n",
       "      <th>PercentFunded</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>InvestmentFromFriendsCount</th>\n",
       "      <th>InvestmentFromFriendsAmount</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1021339766868145413AB3B</td>\n",
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       "      <td>0.1580</td>\n",
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       "      <td>36</td>\n",
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       "      <td>0.28269</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.2400</td>\n",
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       "      <td>36</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.12528</td>\n",
       "      <td>0.0974</td>\n",
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       "      <td>0.2085</td>\n",
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       "<p>5 rows × 81 columns</p>\n",
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       "                ListingKey  ListingNumber            ListingCreationDate  \\\n",
       "0  1021339766868145413AB3B         193129  2007-08-26 19:09:29.263000000   \n",
       "1  10273602499503308B223C1        1209647  2014-02-27 08:28:07.900000000   \n",
       "2  0EE9337825851032864889A          81716  2007-01-05 15:00:47.090000000   \n",
       "3  0EF5356002482715299901A         658116  2012-10-22 11:02:35.010000000   \n",
       "4  0F023589499656230C5E3E2         909464  2013-09-14 18:38:39.097000000   \n",
       "\n",
       "  CreditGrade  Term LoanStatus           ClosedDate  BorrowerAPR  \\\n",
       "0           C    36  Completed  2009-08-14 00:00:00      0.16516   \n",
       "1         NaN    36    Current                  NaN      0.12016   \n",
       "2          HR    36  Completed  2009-12-17 00:00:00      0.28269   \n",
       "3         NaN    36    Current                  NaN      0.12528   \n",
       "4         NaN    36    Current                  NaN      0.24614   \n",
       "\n",
       "   BorrowerRate  LenderYield    ...     LP_ServiceFees  LP_CollectionFees  \\\n",
       "0        0.1580       0.1380    ...            -133.18                0.0   \n",
       "1        0.0920       0.0820    ...               0.00                0.0   \n",
       "2        0.2750       0.2400    ...             -24.20                0.0   \n",
       "3        0.0974       0.0874    ...            -108.01                0.0   \n",
       "4        0.2085       0.1985    ...             -60.27                0.0   \n",
       "\n",
       "   LP_GrossPrincipalLoss  LP_NetPrincipalLoss LP_NonPrincipalRecoverypayments  \\\n",
       "0                    0.0                  0.0                             0.0   \n",
       "1                    0.0                  0.0                             0.0   \n",
       "2                    0.0                  0.0                             0.0   \n",
       "3                    0.0                  0.0                             0.0   \n",
       "4                    0.0                  0.0                             0.0   \n",
       "\n",
       "   PercentFunded  Recommendations InvestmentFromFriendsCount  \\\n",
       "0            1.0                0                          0   \n",
       "1            1.0                0                          0   \n",
       "2            1.0                0                          0   \n",
       "3            1.0                0                          0   \n",
       "4            1.0                0                          0   \n",
       "\n",
       "  InvestmentFromFriendsAmount Investors  \n",
       "0                         0.0       258  \n",
       "1                         0.0         1  \n",
       "2                         0.0        41  \n",
       "3                         0.0       158  \n",
       "4                         0.0        20  \n",
       "\n",
       "[5 rows x 81 columns]"
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   "source": [
    "loan_data.head()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 81 columns):\n",
      "ListingKey                             113937 non-null object\n",
      "ListingNumber                          113937 non-null int64\n",
      "ListingCreationDate                    113937 non-null object\n",
      "CreditGrade                            28953 non-null object\n",
      "Term                                   113937 non-null int64\n",
      "LoanStatus                             113937 non-null object\n",
      "ClosedDate                             55089 non-null object\n",
      "BorrowerAPR                            113912 non-null float64\n",
      "BorrowerRate                           113937 non-null float64\n",
      "LenderYield                            113937 non-null float64\n",
      "EstimatedEffectiveYield                84853 non-null float64\n",
      "EstimatedLoss                          84853 non-null float64\n",
      "EstimatedReturn                        84853 non-null float64\n",
      "ProsperRating (numeric)                84853 non-null float64\n",
      "ProsperRating (Alpha)                  84853 non-null object\n",
      "ProsperScore                           84853 non-null float64\n",
      "ListingCategory (numeric)              113937 non-null int64\n",
      "BorrowerState                          108422 non-null object\n",
      "Occupation                             110349 non-null object\n",
      "EmploymentStatus                       111682 non-null object\n",
      "EmploymentStatusDuration               106312 non-null float64\n",
      "IsBorrowerHomeowner                    113937 non-null bool\n",
      "CurrentlyInGroup                       113937 non-null bool\n",
      "GroupKey                               13341 non-null object\n",
      "DateCreditPulled                       113937 non-null object\n",
      "CreditScoreRangeLower                  113346 non-null float64\n",
      "CreditScoreRangeUpper                  113346 non-null float64\n",
      "FirstRecordedCreditLine                113240 non-null object\n",
      "CurrentCreditLines                     106333 non-null float64\n",
      "OpenCreditLines                        106333 non-null float64\n",
      "TotalCreditLinespast7years             113240 non-null float64\n",
      "OpenRevolvingAccounts                  113937 non-null int64\n",
      "OpenRevolvingMonthlyPayment            113937 non-null float64\n",
      "InquiriesLast6Months                   113240 non-null float64\n",
      "TotalInquiries                         112778 non-null float64\n",
      "CurrentDelinquencies                   113240 non-null float64\n",
      "AmountDelinquent                       106315 non-null float64\n",
      "DelinquenciesLast7Years                112947 non-null float64\n",
      "PublicRecordsLast10Years               113240 non-null float64\n",
      "PublicRecordsLast12Months              106333 non-null float64\n",
      "RevolvingCreditBalance                 106333 non-null float64\n",
      "BankcardUtilization                    106333 non-null float64\n",
      "AvailableBankcardCredit                106393 non-null float64\n",
      "TotalTrades                            106393 non-null float64\n",
      "TradesNeverDelinquent (percentage)     106393 non-null float64\n",
      "TradesOpenedLast6Months                106393 non-null float64\n",
      "DebtToIncomeRatio                      105383 non-null float64\n",
      "IncomeRange                            113937 non-null object\n",
      "IncomeVerifiable                       113937 non-null bool\n",
      "StatedMonthlyIncome                    113937 non-null float64\n",
      "LoanKey                                113937 non-null object\n",
      "TotalProsperLoans                      22085 non-null float64\n",
      "TotalProsperPaymentsBilled             22085 non-null float64\n",
      "OnTimeProsperPayments                  22085 non-null float64\n",
      "ProsperPaymentsLessThanOneMonthLate    22085 non-null float64\n",
      "ProsperPaymentsOneMonthPlusLate        22085 non-null float64\n",
      "ProsperPrincipalBorrowed               22085 non-null float64\n",
      "ProsperPrincipalOutstanding            22085 non-null float64\n",
      "ScorexChangeAtTimeOfListing            18928 non-null float64\n",
      "LoanCurrentDaysDelinquent              113937 non-null int64\n",
      "LoanFirstDefaultedCycleNumber          16952 non-null float64\n",
      "LoanMonthsSinceOrigination             113937 non-null int64\n",
      "LoanNumber                             113937 non-null int64\n",
      "LoanOriginalAmount                     113937 non-null int64\n",
      "LoanOriginationDate                    113937 non-null object\n",
      "LoanOriginationQuarter                 113937 non-null object\n",
      "MemberKey                              113937 non-null object\n",
      "MonthlyLoanPayment                     113937 non-null float64\n",
      "LP_CustomerPayments                    113937 non-null float64\n",
      "LP_CustomerPrincipalPayments           113937 non-null float64\n",
      "LP_InterestandFees                     113937 non-null float64\n",
      "LP_ServiceFees                         113937 non-null float64\n",
      "LP_CollectionFees                      113937 non-null float64\n",
      "LP_GrossPrincipalLoss                  113937 non-null float64\n",
      "LP_NetPrincipalLoss                    113937 non-null float64\n",
      "LP_NonPrincipalRecoverypayments        113937 non-null float64\n",
      "PercentFunded                          113937 non-null float64\n",
      "Recommendations                        113937 non-null int64\n",
      "InvestmentFromFriendsCount             113937 non-null int64\n",
      "InvestmentFromFriendsAmount            113937 non-null float64\n",
      "Investors                              113937 non-null int64\n",
      "dtypes: bool(3), float64(50), int64(11), object(17)\n",
      "memory usage: 68.1+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</th>\n",
       "      <th>Term</th>\n",
       "      <th>BorrowerAPR</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>LenderYield</th>\n",
       "      <th>EstimatedEffectiveYield</th>\n",
       "      <th>EstimatedLoss</th>\n",
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       "      <th>LP_CollectionFees</th>\n",
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       "      <th>LP_NetPrincipalLoss</th>\n",
       "      <th>LP_NonPrincipalRecoverypayments</th>\n",
       "      <th>PercentFunded</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>InvestmentFromFriendsCount</th>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.278857e+05</td>\n",
       "      <td>40.830248</td>\n",
       "      <td>0.218828</td>\n",
       "      <td>0.192764</td>\n",
       "      <td>0.182701</td>\n",
       "      <td>0.168661</td>\n",
       "      <td>0.080306</td>\n",
       "      <td>0.096068</td>\n",
       "      <td>4.072243</td>\n",
       "      <td>5.950067</td>\n",
       "      <td>...</td>\n",
       "      <td>-54.725641</td>\n",
       "      <td>-14.242698</td>\n",
       "      <td>700.446342</td>\n",
       "      <td>681.420499</td>\n",
       "      <td>25.142686</td>\n",
       "      <td>0.998584</td>\n",
       "      <td>0.048027</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>16.550751</td>\n",
       "      <td>80.475228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.280762e+05</td>\n",
       "      <td>10.436212</td>\n",
       "      <td>0.080364</td>\n",
       "      <td>0.074818</td>\n",
       "      <td>0.074516</td>\n",
       "      <td>0.068467</td>\n",
       "      <td>0.046764</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>1.673227</td>\n",
       "      <td>2.376501</td>\n",
       "      <td>...</td>\n",
       "      <td>60.675425</td>\n",
       "      <td>109.232758</td>\n",
       "      <td>2388.513831</td>\n",
       "      <td>2357.167068</td>\n",
       "      <td>275.657937</td>\n",
       "      <td>0.017919</td>\n",
       "      <td>0.332353</td>\n",
       "      <td>0.232412</td>\n",
       "      <td>294.545422</td>\n",
       "      <td>103.239020</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.006530</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.182700</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>-0.182700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-664.870000</td>\n",
       "      <td>-9274.750000</td>\n",
       "      <td>-94.200000</td>\n",
       "      <td>-954.550000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.009190e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.156290</td>\n",
       "      <td>0.134000</td>\n",
       "      <td>0.124200</td>\n",
       "      <td>0.115670</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.074080</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.005540e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.209760</td>\n",
       "      <td>0.184000</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>0.161500</td>\n",
       "      <td>0.072400</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.926340e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.283810</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.224300</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.255725e+06</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.512290</td>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.492500</td>\n",
       "      <td>0.319900</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>0.283700</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>21117.900000</td>\n",
       "      <td>1.012500</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ListingNumber           Term    BorrowerAPR   BorrowerRate  \\\n",
       "count   1.139370e+05  113937.000000  113912.000000  113937.000000   \n",
       "mean    6.278857e+05      40.830248       0.218828       0.192764   \n",
       "std     3.280762e+05      10.436212       0.080364       0.074818   \n",
       "min     4.000000e+00      12.000000       0.006530       0.000000   \n",
       "25%     4.009190e+05      36.000000       0.156290       0.134000   \n",
       "50%     6.005540e+05      36.000000       0.209760       0.184000   \n",
       "75%     8.926340e+05      36.000000       0.283810       0.250000   \n",
       "max     1.255725e+06      60.000000       0.512290       0.497500   \n",
       "\n",
       "         LenderYield  EstimatedEffectiveYield  EstimatedLoss  EstimatedReturn  \\\n",
       "count  113937.000000             84853.000000   84853.000000     84853.000000   \n",
       "mean        0.182701                 0.168661       0.080306         0.096068   \n",
       "std         0.074516                 0.068467       0.046764         0.030403   \n",
       "min        -0.010000                -0.182700       0.004900        -0.182700   \n",
       "25%         0.124200                 0.115670       0.042400         0.074080   \n",
       "50%         0.173000                 0.161500       0.072400         0.091700   \n",
       "75%         0.240000                 0.224300       0.112000         0.116600   \n",
       "max         0.492500                 0.319900       0.366000         0.283700   \n",
       "\n",
       "       ProsperRating (numeric)  ProsperScore      ...        LP_ServiceFees  \\\n",
       "count             84853.000000  84853.000000      ...         113937.000000   \n",
       "mean                  4.072243      5.950067      ...            -54.725641   \n",
       "std                   1.673227      2.376501      ...             60.675425   \n",
       "min                   1.000000      1.000000      ...           -664.870000   \n",
       "25%                   3.000000      4.000000      ...            -73.180000   \n",
       "50%                   4.000000      6.000000      ...            -34.440000   \n",
       "75%                   5.000000      8.000000      ...            -13.920000   \n",
       "max                   7.000000     11.000000      ...             32.060000   \n",
       "\n",
       "       LP_CollectionFees  LP_GrossPrincipalLoss  LP_NetPrincipalLoss  \\\n",
       "count      113937.000000          113937.000000        113937.000000   \n",
       "mean          -14.242698             700.446342           681.420499   \n",
       "std           109.232758            2388.513831          2357.167068   \n",
       "min         -9274.750000             -94.200000          -954.550000   \n",
       "25%             0.000000               0.000000             0.000000   \n",
       "50%             0.000000               0.000000             0.000000   \n",
       "75%             0.000000               0.000000             0.000000   \n",
       "max             0.000000           25000.000000         25000.000000   \n",
       "\n",
       "       LP_NonPrincipalRecoverypayments  PercentFunded  Recommendations  \\\n",
       "count                    113937.000000  113937.000000    113937.000000   \n",
       "mean                         25.142686       0.998584         0.048027   \n",
       "std                         275.657937       0.017919         0.332353   \n",
       "min                           0.000000       0.700000         0.000000   \n",
       "25%                           0.000000       1.000000         0.000000   \n",
       "50%                           0.000000       1.000000         0.000000   \n",
       "75%                           0.000000       1.000000         0.000000   \n",
       "max                       21117.900000       1.012500        39.000000   \n",
       "\n",
       "       InvestmentFromFriendsCount  InvestmentFromFriendsAmount      Investors  \n",
       "count               113937.000000                113937.000000  113937.000000  \n",
       "mean                     0.023460                    16.550751      80.475228  \n",
       "std                      0.232412                   294.545422     103.239020  \n",
       "min                      0.000000                     0.000000       1.000000  \n",
       "25%                      0.000000                     0.000000       2.000000  \n",
       "50%                      0.000000                     0.000000      44.000000  \n",
       "75%                      0.000000                     0.000000     115.000000  \n",
       "max                     33.000000                 25000.000000    1189.000000  \n",
       "\n",
       "[8 rows x 61 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. Analyze number of listings in each state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# count of each state in the data\n",
    "states_count = loan_data.BorrowerState.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the bar graph\n",
    "color = sb.color_palette()[0]\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "states_count.plot(kind='bar', color=color);\n",
    "\n",
    "plt.title('Number of listings in each BorrowerState');\n",
    "plt.xlabel('BorrowerState');\n",
    "plt.ylabel('Number of Listings');\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "The data says the highest listing counts in CA, TX, NY, FL and IL, which tracks with the rankings of top US states by population and the least in WY, ME, ND. In other words, the Prosper listings are distributed similarly to the population of the US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. The number of listings under each LoanStatus available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# count of each status type available\n",
    "\n",
    "status_count = loan_data.LoanStatus.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the bar graph for loan statuses\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "status_count.plot(kind='bar', color=color);\n",
    "\n",
    "plt.title('Number of listings for each LoanStatus');\n",
    "plt.xlabel('LoanStatus');\n",
    "plt.ylabel('Number of Listings');\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "The number of listings in each LoanStatus type are analyzed and the plot shows that Current status has got the highest number of listings followed by Completed and Chargedoff. Other statuses include dues for various intervals and cancelled whose frequency is too less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3. Income rate distribution of listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# count of listings under each income range\n",
    "\n",
    "income_range_count = loan_data.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the bar graph for each income range specified\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "income_range_count.plot(kind='bar', color=color);\n",
    "\n",
    "plt.title('Income rate distribution');\n",
    "plt.xlabel('IncomeRate');\n",
    "plt.ylabel('Number of Listings');\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "The income rate distribution is analyzed to know the average income rate of listings. The plot says that most listings fall under 50000 dollars, followed by 50000-74999 dollars and so on. This also concludes that the more the income rate the less the listings count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    106393.000000\n",
       "mean      11210.225447\n",
       "std       19818.361309\n",
       "min           0.000000\n",
       "25%         880.000000\n",
       "50%        4100.000000\n",
       "75%       13180.000000\n",
       "max      646285.000000\n",
       "Name: AvailableBankcardCredit, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.AvailableBankcardCredit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    113937.000000\n",
       "mean          0.192764\n",
       "std           0.074818\n",
       "min           0.000000\n",
       "25%           0.134000\n",
       "50%           0.184000\n",
       "75%           0.250000\n",
       "max           0.497500\n",
       "Name: BorrowerRate, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.BorrowerRate.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. AvailableBankcardCredit vs. BorrowerRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'AvailableBankcardCredit vs. BorrowerRate')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove the null entries\n",
    "loan_data_sub = loan_data.loc[loan_data.AvailableBankcardCredit.notnull()]\n",
    "\n",
    "# plots the hist2d graph \n",
    "plt.hist2d(data=loan_data_sub, x='AvailableBankcardCredit', y='BorrowerRate', cmap = 'viridis_r', cmin = 0.5);\n",
    "plt.colorbar()\n",
    "\n",
    "plt.xlabel('Available Bank Credit (in USD)')\n",
    "plt.ylabel('BorrowerRate')\n",
    "plt.title('AvailableBankcardCredit vs. BorrowerRate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "In this case, a hist2d plot is used because most of the listings have same range of bank card credit, there needs to be an intense segregation to see the diffusion between them and  the plot says the more the amount in their bank card, the less the loans they take and less the borrower rate in most cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. Income range vs ProsperPrincipalBorrowed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    32192\n",
       "$50,000-74,999    31050\n",
       "$100,000+         17337\n",
       "$75,000-99,999    16916\n",
       "Not displayed      7741\n",
       "$1-24,999          7274\n",
       "Not employed        806\n",
       "$0                  621\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_data.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Prosper Principal Borrowed (in USD)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZjBs3buVdxNixYz3F1azFBussoKrLuyf1VhHxAICkLRnke1JDumuYPn267x4GWH/fKfb0bnAov7sbbAbjLKCqy9NAfAm4WtKDgIDNgU+2NFUJjBs3jksuuaToGEPS6NGjefrpVe/cXG7drBhNGwhJw4AXgTcA25AaiHsj4uU2ZLNBKO+7/T322GPl8wsvvLBFacysmaYNRER0SjopInYD7mhTppbK28WxaNEiADbeeONeP9fdHAOvdhdx4IEHFh3FbMjKU6zvckkfkqSWpymRF198kRdffLHoGEPWpptuyvjx4znyyCOLjmI2ZOUZg/gCsC6wXNJLpG6miIgNWpqsRfK+068Nis6YMaOVcczMSqu3MQgBb4yIR9qUx8zMSqJpF1OkzYAH/eZAZmb2SnnGIK6XtHPLk5iZWankaSAmAn/O9qa+Q9J8SS2Z0SRpXUnzJL2vFd/fzMzyyzNIvW9/v7mkM4H3AU9GxJvqzk8mlQ8fDpweESdmL30FOL+/P89sqHItI2uFPBsGPSxpPPAv2anfR0TeWkxnAz8AzqmdkDQc+CEwCXgMuEnSRcDrgbuBtXOnNzPAtYysNXptICRNBz4D/Co79TNJMyPilN6+NiKukzS22+ldgAUR8WD2/c8DDiBtRrQusD3woqRLI6Iz73/IYOB3gbY6XMuoOIsWLWL4C0tL9987/IUlLFrU//1s8nQxHQa8LSKeB5D0LeDPQK8NRA82Bh6tO34s+/6fy77/ocBTPTUOkqYB0wA222yzfkYoJ78LNLMyydNAiK7VW1dk5/qr0dfGyicRZzf74oiYCcwEmDBhQjT73Cryu0Cz6tl4441Z/PKIUv7b3Xjjjfr99XkaiLOAGyTV1kO8Hzij3z8x3TFsWne8CfDX1fh+ZmbWAnkGqb8n6Rpgd9K7/09GxK2r8TNvAt4gaQtgEXAQ8LHV+H5mZtYCedZBEBG3AKcDfyTdAeQi6VzSeMU2kh6TdFhELAc+B8wB7gHOj4i7+pzczMxaqsc7CEn7AycDTwPHkqamPgGMlfSViJjV2zePiIN7OH8p0O+ObUn7Aft5K1Azs9Zpdgfx38DewOGkxWt7RsSuwI7AF9uQrUcRcXFETBs1alSRMczMBrVmYxCdEfEXAEkP1dYtRMSTkvo/sdbMzCqhWQMxTNKGpLuMzux5bYpqrrELMzOrrmYNxChgHqsahVvqXht06w9saCvzKnbwSnYrRo8NRESMbWOOPvEgtQ20sq5iB69kt+LkWShXOhFxMXDxhAkTPlN0Fhs8yriKHbyS3YpTyQbCzAaXwVrsruo82GxmZg01WyjXtDM2ItwxamYDYrAWu6u6Zl1M80izlXqqvrplSxKZmVkpNJvFtEU7g/SFZzGZmbVerjEISRtK2kXSO2uPVgdrxqU2zMxaL8+Wo58GppP2bbgN2JVUofXdrY1mZmZFyjPNdTqwM3B9REyUtC3w9dbG6p8yr4b1Slgzq5o8DcRLEfGSJCStFRH3Stqm5cn6oayrYYfCStiBbJzBDbRZGeRpIB6T9CrgN8BcSX+nxFuElnE1bNkW/7TCQDbO4AbarAzybDn6gezp1yRdTSriN7ulqaySytg4w9BooL0S2VohV6kNSW8l7UkdwB8j4p8tTdV7Hk9zNTNrsTyzmP4fcCDwq+zUWZIuiIgTWpqsCRfrM+vKK5GtFfLcQRwMvCUiXgKQdCJpb4jCGggzM2u9PAvlFgJr1x2vBTzQkjRmZlYaee4gXgbukjSXNAYxCfiDpJMBIuLoFuYzM7OC5Gkgfp09aq5pTRQzMyuTPNNcZ7UjiJmZlUuz/SDOj4iPSJpP6lrqIiJ2bGkyMzMrVLM7iFqNg/e1I4h5sVORynrtYWhc/8Fg+AtPD8jvz7CXngWgc+0NVvt7pSoC/Z9m3Gw/iMezp8OAx+umuY5crZ84ALxQzszKZCD/Fi1Y8Fz6nlsOxJ/ZjVYrW55B6guAt9cdr8jO7dzvn7qaButCOS92Kk5Zrz0MjetfdQNZCLJWoHLGjBkD9j37K886iBH1pTWy52u2LpKZmZVBngbib5L2rx1IOgB4qnWRzMysDPJ0MR0B/FzSDwABjwJTWprKzMwKl2cdxAPArpLWAxQRz7U+lpmZFS1PNde1gA8BY4ERkgCIiONbmszMzAqVp4vpt8BSYB6pLpOZmQ0BeRqITSJicsuTmJlZqeRpIP4kaYeImN/yNKuprKthh8JK2LJeexga138wGIwrkasuTwOxO3CopIdIXUwCoshaTF5JbTa4DNaVyFWXp4HYt+Up+qinldRlXQ07FFbClvXaw9C4/lU3WFciV12zaq4bRMSzgKe1mpkNQc3uIP6PVMl1Hqnct+peC2DLFuYyM7OCNavm+j6lRQ/viohH2pjJzMxKoOkYRESEpF8DO7Upj5n1k2cB2UDLM0h9vaSdI+Kmlqcxs37xLCBrhTwNxETgCEkLgecpwTRXM+vKs4CsFSo5zdXMzFqv2TTXtUmlvscB84EzIsLLUc3MhohmGwbNAiaQGod9gZPaksjMzEqhWRfT9hGxA4CkM4Ab2xPJzMzKoFkDsaz2JCKW1/aBMBusyjhNFDxV1IrTrIEYL+nZ7LmAkdlxbRbTwPz294OL9dlAK+80UfBUUStKs5XUw9sZpC96KtZn1l+eJmr2Ss0Gqc3MbAhzA2FmZg25gTAzs4aaNhCShku6ol1hzMysPJo2EBGxAnhB0qg25TEzs5LIU4vpJWC+pLmkYn0ARMTRLUtlZmaFy9NAXJI9KqGMi536stCp6vnNbPDotYGIiFmSRgKbRcR9bcjUb+Vd7JRvoVPV85vZ4NJrA5GtWv4usCawhaQ3A8dHxP6tDtdXVV/sVPX8Zja45Jnm+jVgF+AZgIi4DdiihZnMzKwE8jQQyyNiabdz0YowZmZWHnkGqe+U9DFguKQ3AEcDf2ptLDMzK1qeO4ijgDcCLwPnAs8Cn29lKDMzK16eWUwvAP8p6VvpMJ5rfSwzMytar3cQknaWNB+4g7Rg7nZJO7U+mpmZFSnPGMQZwGcj4vcAknYHzgJ2bGUwMzMrVp4G4rla4wAQEX+Q5G4me4WBWgUOXgluVgZ5GogbJZ1GGqAO4KPANZLeChARt7QwX0PecrR8Bvr/hVeCmxUvTwPx5uzjcd3Ov53UYLx7QBPl4C1Hy2cgV4GDV4KblUGeWUwT2xHEzMzKJc8spumSNlByuqRbJO3djnBmZlacPAvlPhURzwJ7A68FPgmc2NJUZmZWuDwNhLKP7wHOiojb686ZmdkglaeBmCfpclIDMUfS+kBna2OZmVnR8sxiOow0k+nBiHhBUgepm8nMzAaxPLOYOiVtAnxMEsC12TRTMzMbxPLMYjoRmA7cnT2OlvTNVgczM7Ni5elieg/w5ojoBJA0C7gVOKaVwczMrFh5BqkBXlX3fFQrgpiZWbnkuYP4JnCrpKtJ01vfie8ezMwGvaYNhNKo9B+AXYGdSQ3EVyJicRuymZlZgZo2EBERkn4TETsBF7Upk5mZlUCeMYjrJe3c8iRmZlYqecYgJgJHSFoIPE/qZoqI8I5yZmaDWJ4GYt+WpzAzs9LpsYGQtDZwBDAOmA+cERHL2xXMzMyK1WwMYhYwgdQ47Auc1JZEZmZWCs26mLaPiB0AJJ0B3NieSGZmVgbN7iCW1Z64a8nMbOhpdgcxXtKz2XMBI7Pj2iymDVqezszMCtNjAxERw9sZxMzMyiVvsT4zMxti3ECYmVlDbiDMzKwhNxBmZtZQaRoISdtJ+rGkX0r616LzmJkNdS1tICSdKelJSXd2Oz9Z0n2SFkj6KkBE3BMRRwAfIa3gNjOzArX6DuJsYHL9CUnDgR+SyndsDxwsafvstf1JGxRd2eJcZmbWC0VEa3+ANBb4XUS8KTveDfhaROyTHR8DEBHfrPuaSyLivb197wkTJsTNN9/cpzynnHIKCxYs6PXzap8zbty4Xj933LhxHHXUUX3K0V8Dnb+d2aH6+fOYPn06ADNmzCg4ySvluf5l/d2Haucv0+++pHkR0WtPTZ5y3wNtY+DRuuPHgLdJ2gP4ILAWcGlPXyxpGjANYLPNNmtZyJEjR7bse7eD81t/Vf3aO//AKeIO4kBgn4j4dHb8CWCXiOhzU9ifOwizVivzHYQZ5L+DKGIW02PApnXHmwB/LSCHmZk1UUQDcRPwBklbSFoTOAi4qIAcZmbWRKunuZ4L/BnYRtJjkg7LSod/DpgD3AOcHxF3tTKHmZn1XUsHqSPi4B7OX0qTgejeSNoP2C/PLAUzM+uf0qyk7ouIuDgipo0aNaroKGZmg1YlGwgzM2s9NxBmZtaQGwgzM2uo5QvlWqE2SA18FLi/hT/q1cBTLfz+reb8xalydnD+orU6/+YR8ZrePqmSDUS7SLo5z2rDsnL+4lQ5Ozh/0cqS311MZmbWkBsIMzNryA1EczOLDrCanL84Vc4Ozl+0UuT3GISZmTXkOwgzM2vIDYSZmTXkBsLMzBoqYsvR0pE0H+hxMCYidmxjnNUmaTQQEfH3orOYWXW5gUjel308Mvv40+zjIcAL7Y/Td5I2A74N7Ak8k05pA+Aq4KsRsbDAeLlI2gr4AGnHweWkVfLnRsTSQoPlIGkYcCjwIdIuibX8P46Ia4pLlp+kiaT89df/9IhYUGiwXkhaD/gyq679P4EHSNf+7AKj9YukYyLim0XnAHcxARARD0fEw8A7IuLLETE/e3wV2KfofDn9Avg1MCYi3hAR44DXAb8Bzis0WQ6SjgZ+DKwN7AyMJP2h+rOkPQqMltcZwGbAN4GrgUuyc8dK6vN+6+0m6URgCnA9sAx4kPRH9oJsH/ky+zkp7z7A14GTgU8AEyV9o8hg/VSe6x0RfmQP4DZg97rjtwO3FZ0rZ/b7+/NaWR7AfGB49nwd4Jrs+WbArUXny5H/jm7H12cf1wLuKTpfnutf93wE8Mfs+YbAnUXn6yX77d2Ob8o+DgPuLTpfP/57bik6Q+3hLqauDgPOlDSKNCaxFPhUsZFymyfpVGAW8Gh2blNSt8etRYXqoxHACtIf1fUBIuIRSWsUmiqfZZK2iogHJL2V1M1BRLwsqQqLjToljY6Ip4HXA8MBIuLvklRstF49L2n3iPhDVsjzaYCI6KxAdgAkPUT6myPgdZIezJ5HRGxZVC43EHUiYh4wPuu7V1Sg77vOFFID93VgY9Iv16PARcCZBebK63TgJknXA+8EvgUg6TVk/+BL7kvA1ZJeAtYADoKV+X9XZLCcvgHcKuk+YFvgX2Fl/tuLDJbDEcDpkrYh3YkeBiuz/7DIYHlFxBa155JujYi3FJmnxiup60jaiPQP5fURsa+k7YHdIuKMgqPlIum1wAsR8Q9JI4F/B9YDZkTE48Wm652kNwLbkbo07i06T19l71Y7IqKSZaaz2W9bAgsi4pmi8wxVbiBKStJlwFnAf0bEeEkjSP3fOxQcLRdJVwGHZt0y3wZeA9wLTI6IicWmyyd711ebBfRQRPyj4Ei5ZbNpJtN1FtDlEdFZaLA+kDSBuvxVaairPAOuO0k/iIjPFZ0DPIupu1dHxPlAJ0BELCf1iZeepKnAVsAe2fOPAjcDi4HNJU2RVNr1HJK2l3QF8GfgBlKX03xJZ2djQqUm6SOk2UuTgc8Bu5Bm0txW5uteI+ldkm4GTiR1SR4OnCHpGkmbFpuuuUEwA66LsjQOgGcx1T+Aa4AOslkEwK7AtUXnypl9c9Ldws7AXsCNpBlAmwM3Zc9HFZ2zSf7rgW2y57sAs7LnnwF+WXS+HPnvANbJnr8amJM93xH4U9H5cuS/FXhN9nwL4NfZ80mku6DCMzbJXukZcFnWbYGvkKbozsieb1d0Lt9BdPXvpEHdrST9ETgHKP0cdkhrOUi/WL8DzgeOj4hHSDMjnoqIR6Lct9sjI+I+gIi4Edghe/4TYPsig+Uk4MXs+fPAawEi4g5gg6JC9cHwiPhb9vwR0hsLImIuadJD2dUm3HSZAUeaMFBqkr5CWqsk0hu7m7Ln50r6apHZPIupTkTMk/QuYBvS/6D7ImJZwbFyi4gfSfop0BkRtRXgS4CDC4yV1wOS/gu4EvggaU0K2RTXKvyeXgrMlnQtsC9wAawc+K3CVMu0JUkPAAAT70lEQVSbJZ1Buv4HkO6mkbQO2ZTXEqv6DLjDgDd2/1sj6XvAXaRuv0J4kLpO1gd7Jmlwy3WM2kjSq4D/IN0t3A6cGBHPZeMP20XE9YUGzEHSe8jyZ++8ayU41oiIlwsN14usIf4Mq67/mRGxIpsN99rsDrW0qjwDTtK9wD7dr7GkzUnde9sUk8wNRBeSxgGfZNUA71mk/0G+SJabpA2B5RHxXNFZhpKqzoCTNBn4AWnmVW2R62bAOOBzETG7sGz+2/dK2bu+9wE/Is1oOpO0lqAKt6uVlF3zqbyyWFwlit1Jej2pK+AA0tqTRdlLZwL/U/auyrqCdx8kXf/KFLzL1iudDIwlG5gmjQFdC0wv+dgbsPL3fxdWLXJ9jFQypNBZlB6k7iabkngS8B3gQuDDwLOkqqjWOmeQBkZPpILF7oCfkbplRpGKrV1I6vIYQTVW89YK3k2megXvzgSOjFSgcndS/aUtgD+SfoeqIOoeK7KPha+f8R1EHUnzSKWyzwAurO83lvSriPhgYeEGOUl3RN2+G5Kuj4hdJa1FKpi4XYHxeiXp9ogYX3c8LyJ2yp7fGxHbFpeudw3y3xQRO2fvbO8uc/4G2W+JiLdmz++OiFLPgpO0N3Aq6Y65due5CamL6bMRcXlR2aowO6SdDoyIBxu94Mah5ape7O5vkj5OutP8ELAQVpbfqMKdepUL3lV9BtwMYK/otmeLpC1Is+MKe3NUhV/cdloi6XuSbs4eJ1VhFe8gUSt29xdS98yXoFLF7j4F7A9cDryNtJoaYDRwTFGh+uAI4HuSniEt0joKKlPw7lOktQ//AbwMTM/Or0MqYll2I0hjDt0touB1HO5iqiPpQuBOUslsSH2w43330B5VL3Zn1h+SjgE+QlosV1+q/yDg/Chwdzk3EHUk3RYRb+7tnLVPVjzu8YhY1Osnl5Ckz5IWK14YqbZXpUg6AFgcETcUnaWvssH1paRtU5cUnaeZbCbW/nSdxXRRRNxdZK4q9M+104u1flgASe9gVfkEK8ZRwI6S/hIRHy06TD+INLPmENIfgKp5G7CDpBERsW/RYfroRlIBy+9T8q6mrCG4O1t5H2VZqOs7iDqS3kzqXhpF+of9NKl8dtk3TBn0JK3vhWc2GEnaDPg28G7SHQ+kv0FXAV/tPnjdTm4gGlDaUY6IeLboLEOZpG9ExH8UnSOvwbQnAYCkcyKi1O+8ayRN5JWLLE+PiAWFBstB0p+B/yVVLV6RnRtOWk/z+YjYtahs7mICJH2hh/MARMT32hpoCJJ0cvdTwCeyFb5ExNHtT5VftifBfqTVuzuTplrW9iT4bNlXg0u6qPsp0iK5VwFERGm7xySdCGxEmuY6BniItAr8guxNxgVF5svh1RHxi/oTWUNxnqT/LigT4DsIACQd1+z1iPh6u7IMVZIeI1UQvZxV1U+/C3wRICJmNf7KcpA0H3hzVuBuHeDSiNgj6z74bZRkC8meSLoFuJtUGTXIyk2T7a0dEdcWl645SfMj2/VRaRfIayPiHVlNrN9HxJuKTdicpPNI3dmz6DqLaSqp8fhIUdl8B4EbgJLYDvhvUqmHL0XEIknHlb1h6GYEqUxClz0JsgVbZTeBtH7gP0nX/zZJL5a5YajTKWl0Vivt9WTlySPi7xVY5AdpAP0wUomT2iymR4GLKbhUiBuIOpK2JK1q3JX0LurPwL/1tLraBk42AP15STsBP5N0CdVayFnpPQki7Zv9fUkXZB+foDp/H74B3CrpPtLObP8KK6996SeYRMQ/SYVBf1R0lu7cxVQn+8f9Q9KtNaTb66Mi4m3FpRp6snd9nwV2i4iPF50nryrvSdCdpPcC76jKJIFseuiWwIKIeKboPAOhDGto3EDUkXRD98agVjSuqExWXdng9KlF58hD0quq/oc1W1S5chbTIGikjyTdEW1e1CQBNxB1stkQz5CWvAdp46C1yGrReD+I1pG0KanE+sbAZcB3ansoSPpNRLy/yHy9aTATTqQaTN+A8s+Ek7ScNEngXNI71so0FkrbBJ9E+re7E6nM94bAMuATEfFoky+3JtxA1JH0UJOXIyK2bFuYIUbSXFKRvutJA3Y7AftFxBJJt1ZgFtBzpMqbd7FqFtbnSfPbSz8RIpuFdQxp//LJwB9IjcVvI6LU1QQk3QrsHRF/yyqgfi8iPiBpEmnAfe+CI/ZK0j7A+0lvkAL4K+naF7abHLiBsJLoXvMqK519DKk8xQW1+v5llU1n/R5p/v3XI+IFSQ9W5U1Ftz0URpLWdBwEvAuYExEfKzJfM6rbSyRbYHZT3X/LXRHxxkID9kLS/wJbA+ewqqrrJqTZTfdHxPSevrbVqjJLoS2yX673krYuXHltyt49MEisIWntiHgJICJ+JmkxMAdYt9hovYuIR4APZ8Xt5kr6ftGZ+mjldNDsjuF84Pys3H2pu/eAmyWdQVoodwCpq4xsPcrwAnPl9Z6I2Lr7SUm/AP7CqvLlbVelaYTtcDFwKNBBmsdee1jrnU4qDLdSRFxBKjdwZyGJ+iEifgtMIv23NKrxX1Y/b3QyIpZWYC3K4cA84O3AFWR7iZC6avYpKlQfvCRplwbndwZeaneYeu5iqqNu216arQ5Jb42IW4rO0V+SxkTE4qJz9EeVrr3SDoo/Ir0Zrb2p2BR4lrTl6LzCsrmBWEXSt4Aro8A9YG2V+n7xKnL+4lQxu6Qx1O0HUYbG2WMQXV0P/Fppo/ZlpP9REREbFBtryKpCmYRmnL84lcqejfW8i7pZTJLmFD3d2GMQXZ0E7AasExEbRMT6bhwKdUnRAVZTqae25vCTogOshspce0lTgFuAPUj7aK8LTATmZa8Vxl1MdSTNAfbN6tKY9Un2LnAyXeeyF/4uMK+sxMkudM1/Y1Tgj0SVr31WQ+pt3bNm1WhvaDTDqV18B9HV48A1ko6R9IXao+hQQ122iKvUyvwuMA9Je5M22fka8B7SdO+vA/dnr5VW1a89WVd2g/OdFNxV5jGIrh7KHmtmD2sTSR/s6SXSJjBl95/ATj29CyQtgiqzGcBe3be3zFYmX0oqQlhWVb/2/wPcIulyVu0HsRlpunShGwa5gahTK4cgad2IeL7oPEPML0hz8Ru9k1q7zVn6o7TvAnMaQeN1G4uAsu9nUelrHxGzsh399mHVLKZrgGMi4u9FZnMDUUfSbqQNOtYDNpM0Hjg8Ij5bbLIh4Q7guxHxikVxkvYqIE9flfZdYE5nkvazOI+uu5odRMGb1uRQ9WtP1hCcV3SO7jxIXUfSDcCHgYtqxeEk3Vn2LQsHA0n/Ajyclazo/tqEiLi5gFh9knVp1L8LfIw0UFrou8C8JG1Pqn1Vn/+iiLi70GA5VP3a96R+O9Ui+A6im4h4tNsuhSuKyjKURMTvJb221r2XFYz7Aml16YyC4+WSbXF5NXUzaar0ByprCO7ONt+JimWv7LUv8/ibG4iuHpX0diAkrQkcDdxTcKah5DxSLaznSTNoXgPcC/wfaVZKaUl6M/BjYBTp3auATSQ9QyqXUOqyD1k12m8D7waWZudGAVcBX+0+eF0mVb/2lHj8zQ1EV0eQ3q1uTPpFuxw4stBEQ4SkqcBWwB7ZfPyPkv5g/QPYPJuueFtE3FFgzGbOJo1X3VB/UtKuwFnA+CJC9cEvSHtXHBIRK2BldeMDSQ13mXdVPJtqX/vSjr95DMJKQdLmpNLenyC9E/wGaTxIwC+BDwFLI2JpYSGbkHR/RLyhh9cWRMS4dmfqi17y9/haGQyCa1/a8TffQVgpRMTDkmYAvyNNq5wSEY9kXR9PNfrHUzKXSbqENOe+fhbQFKDQXcFymifpVGAWXfNPBW4tLFU+lb72ZR5/8x2ElYqk9YDOiHghO14XWKMiJRP2JW1Y030W0KWFBsshG3M7jAb5gTMi4uUC4/WqytceQNJVwKHZm6Jvs2r8bXJEFDb+5gaijqQtIuKh3s6ZmQ2UbPzteOC/SI3b8awafzuOVP6kkPE312Lq6sIG537Z9hRWOZJGSTpR0j2SlmSPe7Jzryo6X28kjZB0uKTLJN0h6fbs+RGSSr2SuurXnrRq+kXSjMlFwBOk3S2vAZZkHx8uIpjHIABJ2wJvBEZ1m5O8AdUo82DFO580JXRibaOXbAOYQ4ELSKt6y+ynwDOk6cW1khubkMYgfkaaVVZWlb72ZR5/cxcToLTR/PtJq0gvqnvpOeC8iPhTIcGsMiTdFxHb9PW1sugl/1+KLDndm6pf+5oyjr/5DoKVG83/VtJuEfHnovNYJT0s6cvArIh4AkDSRqR3sY82+8KS+LukA4ELa/uhKO2seCBQ9hXJVb/2AETEP7odF14w1GMQXT0q6deSnpT0hKQLJW1SdCirhI8CHcC1kp6W9DSp73g08JEig+V0EGndyROS/iLpL8Bi4IPZa2VW9WtfWu5iqiNpLqmsw0+zUx8nrSwtdR+m2UCS1EH62/BU0VmsWG4g6ki6PSLGdzt3W0S8uahMVm3Z+Nbi7mUgqkLSBODxiFhUdJa+qvq1LwOPQXT1N0kfB87Njg8mTTMz66+3ATtIGhER+xYdph+OAnbMBqrLPJOpkapf+8L5DqJONq3sB8BupMqKfwKmR0Qhc5DNykLS+hHxXNE5rL3cQJi1gNJezm8B7o6Ie4vOk0dW3nsydXsqkDbdKX2Zk3pVvPZl5QYCkPT/mrwcEVGJbQutOJJ+ExHvz54fQCqdfQ3wDuAbEXF2cel6l5VTP45U4r423rAJaZHZ1yPinKKy9abq177M3EAAkv69wel1ScXLOiJivTZHsoqRdGvdNrV/Is1+e0jSq4Eru09+KBtJ9wFv6363oLSV5w0lXyhX6WtfZh6kBiLipNpzSesD04FPkjZKOamnrzOrU/9Oa0StwGNEPCWps6BMfSEa72jWmb1WZlW/9qXlBiKjtA/vF4BDSDXx31qVPW2tFMZLepb0x3QtSWMiYnFWRnt4wdny+B/gFkmXs2r18WakLqayd7FW/dqXlruYAEnfIa0YnQn8sPuSd7P+yqqJbleFEi5Zd9I+dN1TYU5V3yhV6dqXlRsIILsNfRlYTtfbVZEGqTcoJJiZWYFciwmIiGERMTIi1o+IDeoe67txsDwkbSrpPEm/l/Qf9XsoSPpNkdnykPSpuucbS7pS0t8l/UlSaQeoAbL6S6dL2lNS2cdLKsUNhNnAOJM0tfIo4HWkwnEd2WubFxWqDz5X9/z7pD0WOoDvAD8qJFF+fwNuI+3E9pikGZJ2LTjToOAGwmxgvCYifhwRt0XEUcCpwHWStqLx7KAy2zoiTouIzoj4Nakqapk9HxE/iIh3kKogLAJOlfSgpG8UnK3SPIvJbGCsIWntiHgJICJ+JmkxMIe0pqbsNpF0Mmnc7TWS1oiIZdlrpd5ylLppuNnua98Gvi1pG8pfqrzU3ECYDYzTScXhrq2diIgrJH2Y1E1Tdl+qe34zsB5pE6ExdN1lsYyubnQyIu4jbaFq/eRZTGYDRNJrSd0dz0saSVpXsz4wIyIeLzZd77L8L0TEP6qWv+rXvqw8BmE2cM4jDexCeuc6jrRd5/8VlqhvzmPVeEPV8lf92peSGwizASBpKrAVsEf2/KOkrprFwOaSpkjasciMzVQ5f5Wzl53HIMwGxjXAi8A9wCjgCeBi0gDq57LXlxaULY9rqG7+a6hu9lJzA2E2ACLiYUkzgN+RZv1MiYhHsk2onspm15RWlfNXOXvZeZDabABJWg/ojIgXsuN1gTWqsulOlfNXOXtZuYEwM7OGPEhtZmYNuYEwM7OG3EDYoCOpVPt5SPqapEWSbpN0t6SDi85klocbCLP2+H5EvBk4ADitvhy4WVm5gbBBS9Iekq6R9EtJ90r6eW2/AEk7Z3sd3C7pRknrS1pb0lmS5ku6VdLE7HMPlfQbSRdLekjS5yR9Ifuc67PtapG0laTZkuZl+0Js2z1TRNwPvABsmH3NZyTdlOW4UNI62fmzJZ2cZXwwq+mEpGGSTpV0l6TfSbq07rWdJF2b/fw5kl7Xjutsg5cbCBvs3gJ8Htge2BJ4h9Jexb8ApkfEeGAv0kKrIwEiYgfgYGCWpLWz7/Mm4GPALqT9m1+IiLcAfwamZJ8zEzgqInYCvkgq+d2FpLcC90fEk9mpX0XEzlmOe4DD6j79dcDuwPuAE7NzHwTGAjsAnyaVtya7IzkF+HD288/Mcpr1mxfK2WB3Y0Q8BiDpNtIf16XA4xFxE0BEPJu9vjvpjywRca+kh4HabmpXR8RzwHOSlpJW6gLMB3bM5uC/HbigblOztepy/Jukz5Aaqcl1598k6QTgVaQKqnPqXvtNRHQCd0vaKDu3O3BBdn6xpFol021Ijdjc7OcPB1ykzlaLGwgb7F6ue76C9DsvGm/i02y7yvrv01l33Jl9z2HAM9k4QyPfj4jvSvogcI6krbK9I84G3h8Rt0s6FNijh5+pbh8bZb8rInZr8t9g1ifuYrKh6F7g9ZJ2BsjGH0YA1wGHZOe2BjYD7svzDbO7kIckHZh9vSSNb/B5vyIVkpuanVofeDzrIjokx4/6A/ChbCxiI1Y1KPeRNvpZ2eUk6Y15spv1xA2EDTkR8U9Sxc9TJN0OzAXWJo0ZDJc0nzRGcWhEvNzzd3qFQ4DDsu95F2nGUiPHA1+QNAz4L+CGLMO9OX7GhcBjwJ3AadnXLs3+mz4MfCv7+beRurzM+s2lNswqRtJ62aY+HcCNwDsiYnHRuWzw8RiEWfX8TtKrgDWB/3bjYK3iOwgzM2vIYxBmZtaQGwgzM2vIDYSZmTXkBsLMzBpyA2FmZg25gTAzs4b+P/N575RVkGx0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# orders the x axis \n",
    "order = ['Not employed', '$0', '$1-24,999', '$25,000-49,999', '$50,000-74,999', '$75,000-99,999', '$100,000+']\n",
    "\n",
    "# plots the boxplot\n",
    "sb.boxplot(data=loan_data, x='IncomeRange', y='ProsperPrincipalBorrowed', color=color, order=order);\n",
    "\n",
    "# scale the y axis with log to analyze more accurately\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.xticks(rotation=90);\n",
    "plt.ylabel('Prosper Principal Borrowed (in USD)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "In this scenario, a boxplot is used to depict the results. The insights from the boxplot are the medians are in increasing order i.e., the more the income range the more the principal amount borrowed from the Prosper except for the not employed or 0 dollar salaried."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3. BorrowerRate pattern over years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Get the year from the creation date and create a column for it\n",
    "\n",
    "loan_data['year'] = loan_data.ListingCreationDate.str.split('-').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "year_data = loan_data.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the pointplot for year vs BorrowerRate\n",
    "\n",
    "sb.pointplot(data=loan_data, x='year', y='BorrowerRate');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "Point plot is used to analyze this and it suites the analysis because this graph shows the nature of the variable from one xfactor to another. For this scenario, it shows that rate of interest increased from 2005-2006 and then fell off from 2006-2007 and then it constantly picked up for few years, reached its peak in 2011 which then started decreasing constantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mutlivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analyse how the BorrowerRate changed over years for Homeowners vs Non-Homeowners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the boxplot to analyze homeowner's and non-homeowner's rate over years\n",
    "\n",
    "g = sb.boxplot(data=loan_data, x='year', y='BorrowerRate', hue='IsBorrowerHomeowner');\n",
    "g.figure.set_figwidth(15)\n",
    "g.figure.set_figheight(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "To exhibit the multivariate analysis, one particular scenario is taken i.e., analysing how the rate of interest changed over years for Homeowners vs Non-Homeowners and to depict this scenario, a boxplot is used because it can be easily analyzed with the advantage of having only two cases in the hue column. This boxplot shows that the borrower rate increased for some years in case of non homeowners, however it constantly decreased around 2010. As regards of homeowners, there wasn't much difference till 2009 but after 2009, the graph went up till 2011 and started falling off from then."
   ]
  }
 ],
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   "language": "python",
   "name": "conda-root-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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