part_one_exploration.ipynb

{
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  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   "source": [
    "# Prosper Loan Data\n",
    "\n",
    "#### by Franck Alain Binde Koube\n",
    "\n",
    "## Dataset Overview\n",
    "\n",
    "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, and many others. All the descriptions about the columns could be find <a href=\"https://docs.google.com/spreadsheets/u/0/d/1gDyi_L4UvIrLTEC6Wri5nbaMmkGmLQBk-Yx3z0XDEtI/edit?usp=sharing\">here</a>. It is strongly recommended to have a look at the variables description to better understand the data.\n",
    "\n",
    "<img src=\"https://images.unsplash.com/photo-1579621970795-87facc2f976d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80\" width=300/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Preliminary Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "sb.set_theme()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b>Loading the dataset</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('prosperLoanData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1021339766868145413AB3B</td>\n",
       "      <td>193129</td>\n",
       "      <td>2007-08-26 19:09:29.263000000</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-08-14 00:00:00</td>\n",
       "      <td>0.16516</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>...</td>\n",
       "      <td>-133.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10273602499503308B223C1</td>\n",
       "      <td>1209647</td>\n",
       "      <td>2014-02-27 08:28:07.900000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12016</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0EE9337825851032864889A</td>\n",
       "      <td>81716</td>\n",
       "      <td>2007-01-05 15:00:47.090000000</td>\n",
       "      <td>HR</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-12-17 00:00:00</td>\n",
       "      <td>0.28269</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0EF5356002482715299901A</td>\n",
       "      <td>658116</td>\n",
       "      <td>2012-10-22 11:02:35.010000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12528</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>...</td>\n",
       "      <td>-108.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0F023589499656230C5E3E2</td>\n",
       "      <td>909464</td>\n",
       "      <td>2013-09-14 18:38:39.097000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24614</td>\n",
       "      <td>0.2085</td>\n",
       "      <td>0.1985</td>\n",
       "      <td>...</td>\n",
       "      <td>-60.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting the first rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113937, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 81 columns):\n",
      " #   Column                               Non-Null Count   Dtype  \n",
      "---  ------                               --------------   -----  \n",
      " 0   ListingKey                           113937 non-null  object \n",
      " 1   ListingNumber                        113937 non-null  int64  \n",
      " 2   ListingCreationDate                  113937 non-null  object \n",
      " 3   CreditGrade                          28953 non-null   object \n",
      " 4   Term                                 113937 non-null  int64  \n",
      " 5   LoanStatus                           113937 non-null  object \n",
      " 6   ClosedDate                           55089 non-null   object \n",
      " 7   BorrowerAPR                          113912 non-null  float64\n",
      " 8   BorrowerRate                         113937 non-null  float64\n",
      " 9   LenderYield                          113937 non-null  float64\n",
      " 10  EstimatedEffectiveYield              84853 non-null   float64\n",
      " 11  EstimatedLoss                        84853 non-null   float64\n",
      " 12  EstimatedReturn                      84853 non-null   float64\n",
      " 13  ProsperRating (numeric)              84853 non-null   float64\n",
      " 14  ProsperRating (Alpha)                84853 non-null   object \n",
      " 15  ProsperScore                         84853 non-null   float64\n",
      " 16  ListingCategory (numeric)            113937 non-null  int64  \n",
      " 17  BorrowerState                        108422 non-null  object \n",
      " 18  Occupation                           110349 non-null  object \n",
      " 19  EmploymentStatus                     111682 non-null  object \n",
      " 20  EmploymentStatusDuration             106312 non-null  float64\n",
      " 21  IsBorrowerHomeowner                  113937 non-null  bool   \n",
      " 22  CurrentlyInGroup                     113937 non-null  bool   \n",
      " 23  GroupKey                             13341 non-null   object \n",
      " 24  DateCreditPulled                     113937 non-null  object \n",
      " 25  CreditScoreRangeLower                113346 non-null  float64\n",
      " 26  CreditScoreRangeUpper                113346 non-null  float64\n",
      " 27  FirstRecordedCreditLine              113240 non-null  object \n",
      " 28  CurrentCreditLines                   106333 non-null  float64\n",
      " 29  OpenCreditLines                      106333 non-null  float64\n",
      " 30  TotalCreditLinespast7years           113240 non-null  float64\n",
      " 31  OpenRevolvingAccounts                113937 non-null  int64  \n",
      " 32  OpenRevolvingMonthlyPayment          113937 non-null  float64\n",
      " 33  InquiriesLast6Months                 113240 non-null  float64\n",
      " 34  TotalInquiries                       112778 non-null  float64\n",
      " 35  CurrentDelinquencies                 113240 non-null  float64\n",
      " 36  AmountDelinquent                     106315 non-null  float64\n",
      " 37  DelinquenciesLast7Years              112947 non-null  float64\n",
      " 38  PublicRecordsLast10Years             113240 non-null  float64\n",
      " 39  PublicRecordsLast12Months            106333 non-null  float64\n",
      " 40  RevolvingCreditBalance               106333 non-null  float64\n",
      " 41  BankcardUtilization                  106333 non-null  float64\n",
      " 42  AvailableBankcardCredit              106393 non-null  float64\n",
      " 43  TotalTrades                          106393 non-null  float64\n",
      " 44  TradesNeverDelinquent (percentage)   106393 non-null  float64\n",
      " 45  TradesOpenedLast6Months              106393 non-null  float64\n",
      " 46  DebtToIncomeRatio                    105383 non-null  float64\n",
      " 47  IncomeRange                          113937 non-null  object \n",
      " 48  IncomeVerifiable                     113937 non-null  bool   \n",
      " 49  StatedMonthlyIncome                  113937 non-null  float64\n",
      " 50  LoanKey                              113937 non-null  object \n",
      " 51  TotalProsperLoans                    22085 non-null   float64\n",
      " 52  TotalProsperPaymentsBilled           22085 non-null   float64\n",
      " 53  OnTimeProsperPayments                22085 non-null   float64\n",
      " 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64\n",
      " 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64\n",
      " 56  ProsperPrincipalBorrowed             22085 non-null   float64\n",
      " 57  ProsperPrincipalOutstanding          22085 non-null   float64\n",
      " 58  ScorexChangeAtTimeOfListing          18928 non-null   float64\n",
      " 59  LoanCurrentDaysDelinquent            113937 non-null  int64  \n",
      " 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64\n",
      " 61  LoanMonthsSinceOrigination           113937 non-null  int64  \n",
      " 62  LoanNumber                           113937 non-null  int64  \n",
      " 63  LoanOriginalAmount                   113937 non-null  int64  \n",
      " 64  LoanOriginationDate                  113937 non-null  object \n",
      " 65  LoanOriginationQuarter               113937 non-null  object \n",
      " 66  MemberKey                            113937 non-null  object \n",
      " 67  MonthlyLoanPayment                   113937 non-null  float64\n",
      " 68  LP_CustomerPayments                  113937 non-null  float64\n",
      " 69  LP_CustomerPrincipalPayments         113937 non-null  float64\n",
      " 70  LP_InterestandFees                   113937 non-null  float64\n",
      " 71  LP_ServiceFees                       113937 non-null  float64\n",
      " 72  LP_CollectionFees                    113937 non-null  float64\n",
      " 73  LP_GrossPrincipalLoss                113937 non-null  float64\n",
      " 74  LP_NetPrincipalLoss                  113937 non-null  float64\n",
      " 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64\n",
      " 76  PercentFunded                        113937 non-null  float64\n",
      " 77  Recommendations                      113937 non-null  int64  \n",
      " 78  InvestmentFromFriendsCount           113937 non-null  int64  \n",
      " 79  InvestmentFromFriendsAmount          113937 non-null  float64\n",
      " 80  Investors                            113937 non-null  int64  \n",
      "dtypes: bool(3), float64(50), int64(11), object(17)\n",
      "memory usage: 68.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Structure of the dataset\n",
    "\n",
    "There are 113937 entries in this dataset for 81 columns. The dataset is diverse, with numeric and non numeric columns.\n",
    "\n",
    "### Main feature(s) of interest\n",
    "\n",
    "I am more interested in understanding the factors that may affect the loan status, the loan original amount and the borrower rate.\n",
    "\n",
    "### Features in the dataset I think will help support my investigation\n",
    "\n",
    "For my investigation, I'll definitely use the `LoanStatus`, `LoanOriginalAmount` and `BorrowerRate` columns. For this, I won't use all the 81 columns. I am more interested in understanding how the following variables might impact my features of interest:\n",
    "\n",
    "- `ListingCategory (numeric)`\n",
    "- `EmploymentStatus`\n",
    "- `Term`\n",
    "- `IsBorrowerHomeowner`\n",
    "- `IncomeRange`\n",
    "- `StatedMonthlyIncome`\n",
    "- `LoanOriginalAmount`\n",
    "- `LoanOriginalDate`\n",
    "- `TotalCreditLinesPast7Years`\n",
    "- `ProsperScore`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the columns of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "cols = ['ListingNumber', 'Term', 'LoanStatus', 'BorrowerRate', 'ListingCategory (numeric)', 'EmploymentStatus',\\\n",
    "        'IsBorrowerHomeowner', 'IncomeRange', 'TotalCreditLinespast7years', 'LoanOriginationDate', \\\n",
    "        'LoanOriginalAmount', 'ProsperScore']\n",
    "\n",
    "df = df[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We want to investigate past loans, not current ones. Therefore, we will exclude current loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Excluding current loans\n",
    "df = df[df.LoanStatus != \"Current\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "We will start by exploring the variable `LoanStatus`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We will create functions that will help us avoid repetition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# function to visualize horizontal barplots\n",
    "def plot_bar(df, col):\n",
    "    series_order = df[col].value_counts().index # finding the order of the serie\n",
    "    sb.countplot(data = df, y = col, color = base_color, order = series_order);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# function to set the figure size\n",
    "def fg(width, height):\n",
    "    return plt.figure(figsize=(width, height))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# function to describe the plots with appropriate axes labels and titles\n",
    "def describe_graph(x_label=\"\", y_label=\"\", title=\"\"):\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(title, fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### How often is each loan status present in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting LoanStatus\n",
    "fg(8, 5)\n",
    "plot_bar(df, 'LoanStatus')\n",
    "describe_graph(title = \"Number of loans per Loan Status\", x_label = \"Count of loans\", y_label = \"Loan Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "In our dataset, we can mainly observe 3 loan status:\n",
    "- Completed\n",
    "- ChargedOff\n",
    "- Defaulted\n",
    "\n",
    "The other status are in a very limited amount. Hence, we will remove rows with loan status different than the mainly ones, in order to reduce noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating list containing the main loan status\n",
    "loan_status = ['Completed', 'Chargedoff', 'Defaulted']\n",
    "df = df.query('LoanStatus in @loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### How is the distribution of the Loan Original Amount?\n",
    "\n",
    "We will use a histogram to see this distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using numpy to specify the bins\n",
    "fg(7, 4)\n",
    "bins = np.arange(0, df.LoanOriginalAmount.max() + 1000, 2000)\n",
    "plt.hist(data = df, x = 'LoanOriginalAmount', bins = bins)\n",
    "describe_graph(x_label = \"Loan Original Amount\", y_label = \"Number of Loans\", title = \"Loan Original Amount Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The number of loan amounts higher than 26K is so low that we barely see it on the histogram. Let's zoom in by setting limits for the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data = df, x = 'LoanOriginalAmount', bins = bins)\n",
    "#setting limits for the x-axis\n",
    "plt.xlim(0, 26000)\n",
    "describe_graph(x_label = \"Loan Original Amount\", y_label = \"Number of Loans\", title = \"Loan Original Amount Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The distribution is <b>right-skewed</b>. The majority of the loans are between \\\\$2000 and $4000. The higher the loan amount, the lesser the count number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### What are important statistics about Borrower Rate?\n",
    "\n",
    "This time we will use a boxplot, in order to visualize summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using seaborn to generate the boxplot\n",
    "sb.boxplot(data = df, y = 'BorrowerRate')\n",
    "describe_graph(title = \"Distribution of BorrowerRate\", y_label = \"BorrowerRate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "50% of the data have a borrower rate of over 20% (slightly). We have a few outliers, representing a borrower rate close to 50%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### How often is each employment status present?\n",
    "\n",
    "A barplot will help us to answer this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating the barplot\n",
    "fg(8, 5)\n",
    "plot_bar(df, 'EmploymentStatus')\n",
    "describe_graph(title=\"Number of loans per employment status\", x_label = \"Count of loans\", y_label = \"Employment status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The majority of the borrowers are <b>employed</b> and work <b>full-time</b>. Many didn't specify their employment status (not available)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Does the number of loans vary based on the income range?\n",
    "First, let's see the different income ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    16343\n",
       "$50,000-74,999    12789\n",
       "Not displayed      7737\n",
       "$75,000-99,999     6442\n",
       "$100,000+          6064\n",
       "$1-24,999          4571\n",
       "$0                  606\n",
       "Not employed        532\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Since there is a natural hierarchy the values can take, we will convert the `IncomeRange` variable to a categorical one. Besides, the two incomes ranges \"Not employed\" and \"\\\\$0\" are ambiguous. Indeed, someone can specify an income range of $0 because he is not employed. Also, the amount of loans with these income ranges are very low. We could also remove them to minimize noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    16343\n",
       "$50,000-74,999    12789\n",
       "Not displayed      7737\n",
       "$75,000-99,999     6442\n",
       "$100,000+          6064\n",
       "$1-24,999          4571\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges = ['Not employed', '$0']\n",
    "df = df.query(\"IncomeRange not in @ranges\")\n",
    "# checking if the filtering worked\n",
    "df.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 60, 12], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Term.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# ordering income_classes\n",
    "income_classes = ['Not displayed', '$1-24,999', '$25,000-49,999', '$50,000-74,999',\\\n",
    "                  '$75,000-99,999', '$100,000+']\n",
    "\n",
    "\n",
    "# creating the categories\n",
    "i_classes = pd.api.types.CategoricalDtype(ordered=True, categories=income_classes)\n",
    "\n",
    "# converting the column to a categorical one\n",
    "df['IncomeRange'] = df['IncomeRange'].astype(i_classes);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# convert cut, color, and clarity into ordered categorical types\n",
    "ordinal_var_dict = {'IncomeRange': ['Not displayed', '$1-24,999', '$25,000-49,999', '$50,000-74,999','$75,000-99,999',\\\n",
    "                               '$100,000+'],\n",
    "                    'Term': [12, 36, 60]\n",
    "                    }\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                categories = ordinal_var_dict[var])\n",
    "    df[var] = df[var].astype(ordered_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['Not displayed', '$1-24,999', '$25,000-49,999',\n",
       "                  '$50,000-74,999', '$75,000-99,999', '$100,000+'],\n",
       ", ordered=True)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking our conversion worked\n",
    "df.IncomeRange.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Now we can use a barchart to answer our question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg(8, 5)\n",
    "sb.countplot(data = df, x = 'IncomeRange', color = base_color)\n",
    "describe_graph(title=\"Number of loans per Income Range\", x_label = \"Income Range\", y_label = \"Count of loans\")\n",
    "plt.xticks(rotation=25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "People with incomes between <b>\\\\$25000</b> and <b>\\\\$49999</b> are mainly present. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### What is the distribution of the Prosper Score?\n",
    "\n",
    "Values of ProsperScore are numeric and discrete. Because of that, a barplot seems more appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg(8, 5)\n",
    "bins = np.arange(1, 11, 1)\n",
    "sb.countplot(data = df, x = 'ProsperScore', color = base_color)\n",
    "describe_graph(x_label = \"ProsperScore\", y_label = \"Count of loans\", title = \"Distribution of ProsperScore\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The distribution is slightly <b>left-skewed</b>, with the majority of loans with a Prosper Score of 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### What are the dominant Listing Categories?\n",
    "\n",
    "The variable `ListingCategory (numeric)` contains numeric values. To ease our understanding, we will match these values with the full name of the category. First we will convert the numeric columns to string (object) type. Then, we will use a dictionnary to do the matching operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Converting ListingCategory to string (object) type\n",
    "df['ListingCategory (numeric)'] = df['ListingCategory (numeric)'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Building a dictionnary to match Listing Category with Actual Full Name Category\n",
    "listings_cat = {\n",
    "    '0': 'Not Available', '1': 'Debt Consolidation', '2': 'Home Improvement', '3': 'Business', '4': 'Personal Loan'\\\n",
    "        , '5': 'Student Use', '6': 'Auto', '7': 'Other', '8': 'Baby&Adoption', '9': 'Boat', '10': 'Cosmetic Procedure'\\\n",
    "        , '11': 'Engagement Ring', '12': 'Green Loans', '13': 'Household Expenses', '14': 'Large Purchases',\\\n",
    "        '15': 'Medical/Dental', '16': 'Motorcycle', '17': 'RV', '18': 'Taxes', '19': 'Vacation', '20': 'Wedding Loans'  \n",
    "} \n",
    "\n",
    "# Function to match Category number with Full name Category\n",
    "def match_cat(key):\n",
    "    return listings_cat[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# creating a new column ListingCategory containing the full name of the categories\n",
    "df['ListingCategory'] = df['ListingCategory (numeric)'].apply(match_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>ListingNumber</th>\n",
       "      <th>Term</th>\n",
       "      <th>LoanStatus</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>ListingCategory (numeric)</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>IsBorrowerHomeowner</th>\n",
       "      <th>IncomeRange</th>\n",
       "      <th>TotalCreditLinespast7years</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>ProsperScore</th>\n",
       "      <th>ListingCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>193129</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>True</td>\n",
       "      <td>$25,000-49,999</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2007-09-12 00:00:00</td>\n",
       "      <td>9425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81716</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>Not available</td>\n",
       "      <td>False</td>\n",
       "      <td>Not displayed</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2007-01-17 00:00:00</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>213551</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>$1-24,999</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2007-10-18 00:00:00</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>577164</td>\n",
       "      <td>36</td>\n",
       "      <td>Defaulted</td>\n",
       "      <td>0.3177</td>\n",
       "      <td>13</td>\n",
       "      <td>Other</td>\n",
       "      <td>True</td>\n",
       "      <td>$50,000-74,999</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2012-04-19 00:00:00</td>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Household Expenses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>31540</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>Not available</td>\n",
       "      <td>True</td>\n",
       "      <td>Not displayed</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2006-08-22 00:00:00</td>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ListingNumber Term  LoanStatus  BorrowerRate ListingCategory (numeric)  \\\n",
       "0          193129   36   Completed        0.1580                         0   \n",
       "2           81716   36   Completed        0.2750                         0   \n",
       "11         213551   36   Completed        0.1325                         0   \n",
       "15         577164   36   Defaulted        0.3177                        13   \n",
       "17          31540   36  Chargedoff        0.1250                         0   \n",
       "\n",
       "   EmploymentStatus  IsBorrowerHomeowner     IncomeRange  \\\n",
       "0     Self-employed                 True  $25,000-49,999   \n",
       "2     Not available                False   Not displayed   \n",
       "11        Full-time                False       $1-24,999   \n",
       "15            Other                 True  $50,000-74,999   \n",
       "17    Not available                 True   Not displayed   \n",
       "\n",
       "    TotalCreditLinespast7years  LoanOriginationDate  LoanOriginalAmount  \\\n",
       "0                         12.0  2007-09-12 00:00:00                9425   \n",
       "2                          3.0  2007-01-17 00:00:00                3001   \n",
       "11                        10.0  2007-10-18 00:00:00                1000   \n",
       "15                        18.0  2012-04-19 00:00:00                4000   \n",
       "17                        36.0  2006-08-22 00:00:00               10000   \n",
       "\n",
       "    ProsperScore     ListingCategory  \n",
       "0            NaN       Not Available  \n",
       "2            NaN       Not Available  \n",
       "11           NaN       Not Available  \n",
       "15           5.0  Household Expenses  \n",
       "17           NaN       Not Available  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We can now remove the numeric Listing Category column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# dropping the previous ListingCategory (numeric) column\n",
    "df.drop(\"ListingCategory (numeric)\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We are plotting a numeric variable against a categorical one. Therefore, we can use a barchart like before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the barplot\n",
    "fg(8, 5)\n",
    "plot_bar(df, 'ListingCategory')\n",
    "describe_graph(title = \"Number of Loans Per ListingCategory\", x_label = \"Count of loans\", y_label = \"Listing Category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The majority of loans are in the <b>Debt Consolidation</b> category. Also, it looks like many borrower don't feel comfortable displaying the reasons behind a loan. \"Not Available\" and \"Other\" are respectively the second and third category with the highest number of loans. \"Business\" and \"Home Improvement\" come next. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Which term period is most represented?\n",
    "\n",
    "The `Term` column is categorical and ordinal. Hence, we will use a barplot and display the categories in their logical order on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the barplot\n",
    "sb.countplot(data = df, x = 'Term', color = base_color)\n",
    "describe_graph(x_label = \"Terms (duration in month)\", y_label = \"Count of loans\", title = \"Number of Loans per Term Period\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Loans with 36 months of Term period are by far the most represented. Let's quickly see what percentage it represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of loans with 36 months of Term Period:\n",
      "90.0\n"
     ]
    }
   ],
   "source": [
    "# group data by Term and count the number of items in each group\n",
    "term_group = df.groupby('Term').count().LoanStatus\n",
    "\n",
    "# get the number of loans for Term 36\n",
    "loans_36 = term_group[36]\n",
    "# calculate the percentage of loans with 36 months of Term period\n",
    "loans_36_percent = round((loans_36 / df.shape[0]) * 100, 0)\n",
    "print(\"Percentage of loans with 36 months of Term Period:\")\n",
    "print(loans_36_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Around <b>90%</b> of the loans have 36 months as Term Period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### How distributed is the total credit lines received during the past 7 years?\n",
    "\n",
    "Our variable of interest `TotalCreditLinespast7years` is numeric. Having an overview of summary statistics could be helpful. We can go with a <b>violin plot</b> to display that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating the violin plot\n",
    "sb.violinplot(data = df, y = 'TotalCreditLinespast7years', inner = 'quartile')\n",
    "describe_graph(title = \"Distribution of TotalCreditLinespast7years\", y_label = \"# TotalCreditLinespast7years\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The plot clearly shows that the distribution is <b>right-skewed</b>. 50% of loans are from borrowers wih less than 20 total credit line for the past 7 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Are more loans issued during particular months?\n",
    "\n",
    "We know the date a loan was generated. We will convert the `LoanOriginationDate` to a datetime column. We will then extract the month to perform our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# converting to datetime \n",
    "df.LoanOriginationDate = pd.to_datetime(df.LoanOriginationDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# creating a new column containing the month a loan was originated\n",
    "df['monthLoanOrigination'] = df['LoanOriginationDate'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# grouping by month, and counting the number of loans for each month\n",
    "month_loan = df.groupby('monthLoanOrigination').count().Term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "When dealing with months, we directly think of chronology. Because of that, a <b>linechart</b> is the best choice for our task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating the line plot\n",
    "fg(8, 5)\n",
    "plt.plot(month_loan.index, month_loan)\n",
    "# setting ylim to better see the values\n",
    "plt.ylim(0, 6000)\n",
    "describe_graph(x_label = \"Month_number\", y_label = \"Number of loans\", title = \"Number of generated loans by Month\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b>August</b> is the month with most of the originated loans (around 5200 loans). With around 3800 loans generated, <b>February</b> ranks last in this category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> We will go a little further with our exploration. The univariate exploration generated even more questions that might need investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Is there a correlation between our numeric variables?\n",
    "\n",
    "In the previous steps, we individually looked at our numeric variables. However, it could be important to see if two of them move in the same direction or not. Since we are talking about correlations between numeric variables, we could use a <b>heatmap</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specifying our numeric columns\n",
    "numeric_vars = ['BorrowerRate', 'TotalCreditLinespast7years', 'LoanOriginalAmount', 'ProsperScore']\n",
    "# generating the heatmap\n",
    "fg(8, 5)\n",
    "sb.heatmap(df[numeric_vars].corr(), annot = True, fmt = '.3f', cmap = 'vlag_r', center = 0)\n",
    "describe_graph(title = \"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The only correlation that needs our attention is the one between `ProsperScore` and `BorrowerRate`. There is a relatively strong and negative correlation (r = -0.717) between these two variables. That means, when the ProsperScore increases, the Borrower Rate tend to decrease. This is understandable. A higher prosper score means a loan with less risk. The lower the interest rate (low `BorrowerRate`), the lower the risk (high `ProsperScore`) to pay back. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Hos does the borrower rate impact the loan status?\n",
    "\n",
    "Our variable of interests are categorical and numeric. A boxplot seems appropriate to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the boxplot\n",
    "plt.figure(figsize = (8, 4))\n",
    "sb.boxplot(data=df, x='LoanStatus', y='BorrowerRate', color = base_color)\n",
    "describe_graph(title = \"Borrower Rate Distribution per Loan Status\", x_label = \"Loan Status\", y_label = \"Borrower Rate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The 1st, 2rd, 3rd quartile, and the maximum value for the borrower rate of <b>Completed</b> loans are all <b>lower</b> that the ones of defaulted and chargedoff loans. In general, people with lower interest rate for their loan tend to complete the payment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Does employment status play a role in the loan original amount?\n",
    "\n",
    "We suspect employed people to have higher loan amounts. That would be logical. But let's see if the data tells the same story.\n",
    "\n",
    "The variable types are the same as in the previous case. So we will use boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the boxplots\n",
    "fg(15, 4)\n",
    "sb.boxplot(data=df, x='EmploymentStatus', y='LoanOriginalAmount', color = base_color)\n",
    "describe_graph(title = \"Loan Original Amount Distribution per Employment Status\", \\\n",
    "               x_label = \"Employment Status\", y_label = \"Loan Original Amount\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Employed people tend to have higher loan amounts. The plot doesn't seem to show anything unexpected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Does the term impact the loan original amount?\n",
    "\n",
    "The `Term` variable is numeric and ordinal. So our plot will show the natural order (12 - 36 - 60) for term period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the boxplot\n",
    "fg(8, 4)\n",
    "sb.boxplot(data=df, x='Term', y='LoanOriginalAmount', color = base_color)\n",
    "describe_graph(title = \"Loan Original Amount Distribution per Term\", x_label = \"Terms in months\", \\\n",
    "               y_label = \"Loan Original Amount\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Statistically, the graph shows expected trends. Long term loans are in general more expensive (since there is more time to complete the payment). We see that the median of the loan amount for a 60-months loan is much higher that the ones for 12 and 36 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "We already had a look at the relationship between the loan amount and the loan status. To go even further, we could try to see how these variables might change based on the fact of owning a home, or the employment status. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Do combined features like owning a home and the loan original amount impact the loan status?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We need a three dimensional plot to investigate this. With one numeric and two categorical variables, violinplots make perfect sense. Also because violinplots add kernel density plot to the summary statistics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating boxplots\n",
    "fg(10, 6)\n",
    "sb.violinplot(data=df, x='LoanStatus', y='LoanOriginalAmount', color = base_color, hue = \"IsBorrowerHomeowner\")\n",
    "describe_graph(title = \"Distribution of Loan Original Amount per Loan Status\", \\\n",
    "               x_label = \"Loan Status\", y_label = \"Loan Original Amount\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Regardless of the loan status, <b>home owners tend to acquire more expensive loans</b>. Indeed, the median and 3rd quartile of loan amounts are higher for home owners. On the other hand, owning a home doesn't seem to have a significant impact on the loan status. The main fact we can mention is the long trail for completed loans (showing very high value for loan amounts). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Do combined features like employment status and the loan original amount impact the loan status?\n",
    "\n",
    "This investigation is among the most interesting ones. We know from experience that the employment status plays a role if someone wants to first obtain a loan. We have already explored each of these variables alone. Let's see how they interact. The variables types make the use of boxplot appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the plots\n",
    "fg(20, 6)\n",
    "sb.boxplot(data=df, x='EmploymentStatus', y='LoanOriginalAmount', color = base_color, hue = \"LoanStatus\")\n",
    "describe_graph(title = \"Distribution of Loan Original Amount Empoyment Status\", \\\n",
    "               x_label = \"Employment Status\", y_label = \"Loan Original Amount\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What are some key findings:\n",
    "- <b>Self-employed</b> people with <b>completed loans</b> have a much lower median loan amount than self-employed with defaulted loans.\n",
    "- The highest Interquartile Range is definitely for <b>self-employed</b> with <b>defaulted loans</b>. This means that most of the loans for the subset of the data are very spread. "
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    "## Conclusions\n",
    "\n",
    "- The only correlation that needs our attention is the one between `ProsperScore` and `BorrowerRate`. There is a relatively strong and negative correlation (r = -0.717) between these two variables. That means, when the ProsperScore increases, the Borrower Rate tend to decrease. This is understandable. A higher prosper score means a loan with less risk. The lower the interest rate (low `BorrowerRate`), the lower the risk (high `ProsperScore`) to pay back. \n",
    "\n",
    "- The 1st, 2rd, 3rd quartile, and the maximum value for the borrower rate of <b>Completed</b> loans are all <b>lower</b> that the ones of defaulted and chargedoff loans. In general, people with lower interest rate for their loan tend to complete the payment.  \n",
    "\n",
    "- Long term loans are in general more expensive (since there is more time to complete the payment). We see that the median of the loan amount for a 60-months loan is much higher that the ones for 12 and 36 months.\n",
    "\n",
    "- Regardless of the loan status, <b>home owners tend to acquire more expensive loans</b>. Indeed, the median and 3rd quartile of loan amounts are higher for home owners. On the other hand, owning a home doesn't seem to have a significant impact on the loan status. \n",
    "\n",
    "- <b>Self-employed</b> people with <b>completed loans</b> have a much lower median loan amount than self-employed with defaulted loans."
   ]
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