EDA_PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Exploratory Data Analysis Mini Project </h1>\n",
    "     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 class=\"list-group-item list-group-item-action active\" >1. DESCRIPTIVE STATISTICS</h4>\n",
    "\n",
    ">#### 1. Create a dataframe using below data and answer the below questions:\n",
    "\n",
    "        Hourly_Income = [1000,2009,24418,444478,324235,243242,3434234,7567457,9235,238237,1312,3412]\n",
    "        Hourly_Expense =  [651361,217371,2746,2356,13436,5732,346346,3463,1132,23534,242235,235235]\n",
    "        family_members_count = [3,4,2,3,1,4,5,6,3,6,3,5]\n",
    "        House_rent = [1299,2300,3411,3422,4566,4211,4600,736,672,0,734,2374]\n",
    "        Highest_income_Member = [\"Olivia\",\"George\",\"Isla\",\"Harry\",\"Ava\",\"Noah\",\"Sophia\",\"Jacobi\",\"Freddie\",\"Ella\",\"Grace\",\"Ella\"]\n",
    "        \n",
    "        \n",
    ">##### A. Display the five point summary of the data. \n",
    ">##### B. What is the mean of the hourly expense?\n",
    ">##### C. What is the median of the hourly expense?\n",
    ">##### D. Find the family member with maximum income and using a suitable graph.\n",
    ">##### E. Calculate IQR(the difference between 75% and 25% quartile) for `Hourly_Income` and `Hourly_Expense`.\n",
    ">##### F. Calculate the standard deviation for first 2 columns.\n",
    ">##### G. Calculate variance for the first 4 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hourly_Income</th>\n",
       "      <th>Hourly_Expense</th>\n",
       "      <th>family_members_count</th>\n",
       "      <th>House_rent</th>\n",
       "      <th>Highest_income_Member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>651361</td>\n",
       "      <td>3</td>\n",
       "      <td>1299</td>\n",
       "      <td>Olivia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>217371</td>\n",
       "      <td>4</td>\n",
       "      <td>2300</td>\n",
       "      <td>George</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24418</td>\n",
       "      <td>2746</td>\n",
       "      <td>2</td>\n",
       "      <td>3411</td>\n",
       "      <td>Isla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444478</td>\n",
       "      <td>2356</td>\n",
       "      <td>3</td>\n",
       "      <td>3422</td>\n",
       "      <td>Harry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>324235</td>\n",
       "      <td>13436</td>\n",
       "      <td>1</td>\n",
       "      <td>4566</td>\n",
       "      <td>Ava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>243242</td>\n",
       "      <td>5732</td>\n",
       "      <td>4</td>\n",
       "      <td>4211</td>\n",
       "      <td>Noah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3434234</td>\n",
       "      <td>346346</td>\n",
       "      <td>5</td>\n",
       "      <td>4600</td>\n",
       "      <td>Sophia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7567457</td>\n",
       "      <td>3463</td>\n",
       "      <td>6</td>\n",
       "      <td>736</td>\n",
       "      <td>Jacobi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9235</td>\n",
       "      <td>1132</td>\n",
       "      <td>3</td>\n",
       "      <td>672</td>\n",
       "      <td>Freddie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>238237</td>\n",
       "      <td>23534</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Ella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1312</td>\n",
       "      <td>242235</td>\n",
       "      <td>3</td>\n",
       "      <td>734</td>\n",
       "      <td>Grace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3412</td>\n",
       "      <td>235235</td>\n",
       "      <td>5</td>\n",
       "      <td>2374</td>\n",
       "      <td>Ella</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hourly_Income  Hourly_Expense  family_members_count  House_rent  \\\n",
       "0            1000          651361                     3        1299   \n",
       "1            2009          217371                     4        2300   \n",
       "2           24418            2746                     2        3411   \n",
       "3          444478            2356                     3        3422   \n",
       "4          324235           13436                     1        4566   \n",
       "5          243242            5732                     4        4211   \n",
       "6         3434234          346346                     5        4600   \n",
       "7         7567457            3463                     6         736   \n",
       "8            9235            1132                     3         672   \n",
       "9          238237           23534                     6           0   \n",
       "10           1312          242235                     3         734   \n",
       "11           3412          235235                     5        2374   \n",
       "\n",
       "   Highest_income_Member  \n",
       "0                 Olivia  \n",
       "1                 George  \n",
       "2                   Isla  \n",
       "3                  Harry  \n",
       "4                    Ava  \n",
       "5                   Noah  \n",
       "6                 Sophia  \n",
       "7                 Jacobi  \n",
       "8                Freddie  \n",
       "9                   Ella  \n",
       "10                 Grace  \n",
       "11                  Ella  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(\n",
    "  {\n",
    "      \"Hourly_Income\":[1000,2009,24418,444478,324235,243242,3434234,7567457,9235,238237,1312,3412],\n",
    "    \"Hourly_Expense\": [651361,217371,2746,2356,13436,5732,346346,3463,1132,23534,242235,235235],\n",
    "      \"family_members_count\":[3,4,2,3,1,4,5,6,3,6,3,5],\n",
    "      \"House_rent\":[1299,2300,3411,3422,4566,4211,4600,736,672,0,734,2374],\n",
    "      \"Highest_income_Member\":[\"Olivia\",\"George\",\"Isla\",\"Harry\",\"Ava\",\"Noah\",\"Sophia\",\"Jacobi\",\"Freddie\",\"Ella\",\"Grace\",\"Ella\"]\n",
    "  }\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Display the five point summary of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hourly_Income</th>\n",
       "      <th>Hourly_Expense</th>\n",
       "      <th>family_members_count</th>\n",
       "      <th>House_rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.024439e+06</td>\n",
       "      <td>145412.250000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2360.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.274083e+06</td>\n",
       "      <td>202831.565333</td>\n",
       "      <td>1.544786</td>\n",
       "      <td>1661.663813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1132.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.061250e+03</td>\n",
       "      <td>3283.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>735.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.313275e+05</td>\n",
       "      <td>18485.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.542958e+05</td>\n",
       "      <td>236985.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3619.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.567457e+06</td>\n",
       "      <td>651361.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Hourly_Income  Hourly_Expense  family_members_count   House_rent\n",
       "count   1.200000e+01       12.000000             12.000000    12.000000\n",
       "mean    1.024439e+06   145412.250000              3.750000  2360.416667\n",
       "std     2.274083e+06   202831.565333              1.544786  1661.663813\n",
       "min     1.000000e+03     1132.000000              1.000000     0.000000\n",
       "25%     3.061250e+03     3283.750000              3.000000   735.500000\n",
       "50%     1.313275e+05    18485.000000              3.500000  2337.000000\n",
       "75%     3.542958e+05   236985.000000              5.000000  3619.250000\n",
       "max     7.567457e+06   651361.000000              6.000000  4600.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. What is the mean of the hourly expense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145412.25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Hourly_Expense\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. What is the median of the hourly expense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18485.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Hourly_Expense\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Find the family member with maximum income and using a suitable graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x260a5daf070>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df[\"Highest_income_Member\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E.Calculate IQR(the difference between 75% and 25% quartile) for `Hourly_Income` and `Hourly_Expense`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233923.75"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR=df[\"Hourly_Expense\"].quantile(0.75)-df[\"Hourly_Income\"].quantile(0.25)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F. Calculate the standard deviation for first 2 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hourly_Income</th>\n",
       "      <th>Hourly_Expense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.274083e+06</td>\n",
       "      <td>202831.565333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hourly_Income  Hourly_Expense\n",
       "0   2.274083e+06   202831.565333"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.iloc[:,0:2].std().to_frame()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### G. Calculate variance for the first 4 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hourly_Income</th>\n",
       "      <th>Hourly_Expense</th>\n",
       "      <th>family_members_count</th>\n",
       "      <th>House_rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.171452e+12</td>\n",
       "      <td>4.114064e+10</td>\n",
       "      <td>2.386364</td>\n",
       "      <td>2.761127e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hourly_Income  Hourly_Expense  family_members_count    House_rent\n",
       "0   5.171452e+12    4.114064e+10              2.386364  2.761127e+06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.iloc[:,0:4].var().to_frame()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 class=\"list-group-item list-group-item-action active\" >2. Preliminary Wrangling,</h4>\n",
    "\n",
    ">### Dataset Information: \n",
    "\n",
    "Dataset recording people to invest in each other in a way that is financially and socially rewarding. On loans, borrowers list loan requests between $ 2,000 and $ 35,000 and individual investors invest as little as $ 25 in each loan listing they select. Prosper handles the servicing of the loan on behalf of the matched borrowers and investors.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    ">#### A. Read the dataset called Pri-Load.csv\n",
    ">#### B.  Check the data type and adjust datatype for all other categorical columns.\n",
    ">#### C. If you find any of the missing values in the ProsperRating column, then drop it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingCreationDate</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>ProsperRating (Alpha)</th>\n",
       "      <th>ProsperScore</th>\n",
       "      <th>ListingCategory (numeric)</th>\n",
       "      <th>...</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>AmountDelinquent</th>\n",
       "      <th>StatedMonthlyIncome</th>\n",
       "      <th>LoanCurrentDaysDelinquent</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>LoanOriginationQuarter</th>\n",
       "      <th>MonthlyLoanPayment</th>\n",
       "      <th>LP_InterestandFees</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-08-26 19:09:29.263000000</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>14-08-2009 0.00</td>\n",
       "      <td>0.16516</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>9425</td>\n",
       "      <td>12-09-2007 0.00</td>\n",
       "      <td>Q3 2007</td>\n",
       "      <td>330.43</td>\n",
       "      <td>1971.14</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-02-27 08:28:07.900000000</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>A</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6125.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>03-03-2014 0.00</td>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>318.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-05 15:00:47.090000000</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>17-12-2009 0.00</td>\n",
       "      <td>0.28269</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Not available</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>3001</td>\n",
       "      <td>17-01-2007 0.00</td>\n",
       "      <td>Q1 2007</td>\n",
       "      <td>123.32</td>\n",
       "      <td>1185.63</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-10-22 11:02:35.010000000</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>A</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>Employed</td>\n",
       "      <td>10056.0</td>\n",
       "      <td>2875.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>01-11-2012 0.00</td>\n",
       "      <td>Q4 2012</td>\n",
       "      <td>321.45</td>\n",
       "      <td>1052.11</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-14 18:38:39.097000000</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>D</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9583.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>20-09-2013 0.00</td>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>563.97</td>\n",
       "      <td>1256.63</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ListingCreationDate  Term LoanStatus       ClosedDate  \\\n",
       "0  2007-08-26 19:09:29.263000000    36  Completed  14-08-2009 0.00   \n",
       "1  2014-02-27 08:28:07.900000000    36    Current              NaN   \n",
       "2  2007-01-05 15:00:47.090000000    36  Completed  17-12-2009 0.00   \n",
       "3  2012-10-22 11:02:35.010000000    36    Current              NaN   \n",
       "4  2013-09-14 18:38:39.097000000    36    Current              NaN   \n",
       "\n",
       "   BorrowerAPR  BorrowerRate  LenderYield ProsperRating (Alpha)  ProsperScore  \\\n",
       "0      0.16516        0.1580       0.1380                   NaN           NaN   \n",
       "1      0.12016        0.0920       0.0820                     A           7.0   \n",
       "2      0.28269        0.2750       0.2400                   NaN           NaN   \n",
       "3      0.12528        0.0974       0.0874                     A           9.0   \n",
       "4      0.24614        0.2085       0.1985                     D           4.0   \n",
       "\n",
       "   ListingCategory (numeric)  ... EmploymentStatus AmountDelinquent  \\\n",
       "0                          0  ...    Self-employed            472.0   \n",
       "1                          2  ...         Employed              0.0   \n",
       "2                          0  ...    Not available              NaN   \n",
       "3                         16  ...         Employed          10056.0   \n",
       "4                          2  ...         Employed              0.0   \n",
       "\n",
       "  StatedMonthlyIncome  LoanCurrentDaysDelinquent  LoanOriginalAmount  \\\n",
       "0         3083.333333                          0                9425   \n",
       "1         6125.000000                          0               10000   \n",
       "2         2083.333333                          0                3001   \n",
       "3         2875.000000                          0               10000   \n",
       "4         9583.333333                          0               15000   \n",
       "\n",
       "   LoanOriginationDate  LoanOriginationQuarter MonthlyLoanPayment  \\\n",
       "0      12-09-2007 0.00                 Q3 2007             330.43   \n",
       "1      03-03-2014 0.00                 Q1 2014             318.93   \n",
       "2      17-01-2007 0.00                 Q1 2007             123.32   \n",
       "3      01-11-2012 0.00                 Q4 2012             321.45   \n",
       "4      20-09-2013 0.00                 Q3 2013             563.97   \n",
       "\n",
       "  LP_InterestandFees  Investors  \n",
       "0            1971.14        258  \n",
       "1               0.00          1  \n",
       "2            1185.63         41  \n",
       "3            1052.11        158  \n",
       "4            1256.63         20  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Pri-Load.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 22)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B.  Check the data type and adjust datatype for all other categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1035 entries, 0 to 1034\n",
      "Data columns (total 22 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   ListingCreationDate        1035 non-null   object \n",
      " 1   Term                       1035 non-null   int64  \n",
      " 2   LoanStatus                 1035 non-null   object \n",
      " 3   ClosedDate                 479 non-null    object \n",
      " 4   BorrowerAPR                1035 non-null   float64\n",
      " 5   BorrowerRate               1035 non-null   float64\n",
      " 6   LenderYield                1035 non-null   float64\n",
      " 7   ProsperRating (Alpha)      783 non-null    object \n",
      " 8   ProsperScore               783 non-null    float64\n",
      " 9   ListingCategory (numeric)  1035 non-null   int64  \n",
      " 10  BorrowerState              983 non-null    object \n",
      " 11  Occupation                 996 non-null    object \n",
      " 12  EmploymentStatus           1010 non-null   object \n",
      " 13  AmountDelinquent           954 non-null    float64\n",
      " 14  StatedMonthlyIncome        1035 non-null   float64\n",
      " 15  LoanCurrentDaysDelinquent  1035 non-null   int64  \n",
      " 16  LoanOriginalAmount         1035 non-null   int64  \n",
      " 17  LoanOriginationDate        1035 non-null   object \n",
      " 18  LoanOriginationQuarter     1035 non-null   object \n",
      " 19  MonthlyLoanPayment         1035 non-null   float64\n",
      " 20  LP_InterestandFees         1035 non-null   float64\n",
      " 21  Investors                  1035 non-null   int64  \n",
      "dtypes: float64(8), int64(5), object(9)\n",
      "memory usage: 178.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adjust datatype for all other categorical columns\n",
    "df['Occupation'] = df['Occupation'].astype('category')\n",
    "df['BorrowerState'] = df['BorrowerState'].astype('category')\n",
    "df['LoanStatus'] = df['LoanStatus'].astype('category')\n",
    "df['EmploymentStatus'] = df['EmploymentStatus'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1035 entries, 0 to 1034\n",
      "Data columns (total 22 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   ListingCreationDate        1035 non-null   object  \n",
      " 1   Term                       1035 non-null   int64   \n",
      " 2   LoanStatus                 1035 non-null   category\n",
      " 3   ClosedDate                 479 non-null    object  \n",
      " 4   BorrowerAPR                1035 non-null   float64 \n",
      " 5   BorrowerRate               1035 non-null   float64 \n",
      " 6   LenderYield                1035 non-null   float64 \n",
      " 7   ProsperRating (Alpha)      783 non-null    object  \n",
      " 8   ProsperScore               783 non-null    float64 \n",
      " 9   ListingCategory (numeric)  1035 non-null   int64   \n",
      " 10  BorrowerState              983 non-null    category\n",
      " 11  Occupation                 996 non-null    category\n",
      " 12  EmploymentStatus           1010 non-null   category\n",
      " 13  AmountDelinquent           954 non-null    float64 \n",
      " 14  StatedMonthlyIncome        1035 non-null   float64 \n",
      " 15  LoanCurrentDaysDelinquent  1035 non-null   int64   \n",
      " 16  LoanOriginalAmount         1035 non-null   int64   \n",
      " 17  LoanOriginationDate        1035 non-null   object  \n",
      " 18  LoanOriginationQuarter     1035 non-null   object  \n",
      " 19  MonthlyLoanPayment         1035 non-null   float64 \n",
      " 20  LP_InterestandFees         1035 non-null   float64 \n",
      " 21  Investors                  1035 non-null   int64   \n",
      "dtypes: category(4), float64(8), int64(5), object(5)\n",
      "memory usage: 155.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. If you find any of the missing values in the ProsperRating column, then drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ListingCreationDate            0\n",
       "Term                           0\n",
       "LoanStatus                     0\n",
       "ClosedDate                   556\n",
       "BorrowerAPR                    0\n",
       "BorrowerRate                   0\n",
       "LenderYield                    0\n",
       "ProsperRating (Alpha)        252\n",
       "ProsperScore                 252\n",
       "ListingCategory (numeric)      0\n",
       "BorrowerState                 52\n",
       "Occupation                    39\n",
       "EmploymentStatus              25\n",
       "AmountDelinquent              81\n",
       "StatedMonthlyIncome            0\n",
       "LoanCurrentDaysDelinquent      0\n",
       "LoanOriginalAmount             0\n",
       "LoanOriginationDate            0\n",
       "LoanOriginationQuarter         0\n",
       "MonthlyLoanPayment             0\n",
       "LP_InterestandFees             0\n",
       "Investors                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                          0\n",
       "ListingCreationDate            0\n",
       "Term                           0\n",
       "LoanStatus                     0\n",
       "ClosedDate                   556\n",
       "BorrowerAPR                    0\n",
       "BorrowerRate                   0\n",
       "LenderYield                    0\n",
       "ProsperRating (Alpha)          0\n",
       "ProsperScore                   0\n",
       "ListingCategory (numeric)      0\n",
       "BorrowerState                  0\n",
       "Occupation                    14\n",
       "EmploymentStatus               0\n",
       "AmountDelinquent               0\n",
       "StatedMonthlyIncome            0\n",
       "LoanCurrentDaysDelinquent      0\n",
       "LoanOriginalAmount             0\n",
       "LoanOriginationDate            0\n",
       "LoanOriginationQuarter         0\n",
       "MonthlyLoanPayment             0\n",
       "LP_InterestandFees             0\n",
       "Investors                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['ProsperRating (Alpha)']).reset_index()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 class=\"list-group-item list-group-item-action active\" >3. UNIVARIATE ANALYSIS</h4>\n",
    "\n",
    ">#### A. What are the main features of interest in your dataset?\n",
    "\n",
    "    Step1: Apply Univariate analysis using suitable charts for[ Loan status, Employment Status, Stated Monthly Income]\n",
    "\n",
    "    Step2: Check if any column distribution skew or not.\n",
    "\n",
    "    Step3: Write at least 2 observations for each visualization.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N/DkNahthnk79Xkk6EngpcB6wELgVWBt4ErBHc/s9ti8bYaZplGnmrwOeAdwLrAXcBnwHONb2taPK09VMtXN1akrqiLy0ud4U2BX4fnN/D8o/oCpFAfge5fT1dSX9rq9dgG1vUCHTx5vrtYG5wKVNnqcCFwDPGXUgSc+m/H+bIendfQ9tQJnGXEvXfq8usn3kch77hKRNga1GmAfgXOA/gMOBK2w/ACBpY8rP6SOSTrP95THPVDXX2B0p9Ej6FvBm20ua+5sDn7a9wm98Q8yzlu17JZ1ue58aGZanWZjwKNuXN/efArzX9hsqZHkesDvwVuAzfQ/dBZxZ41tdv679XvVrvn2uZ/t3K33ycD5/Tdv3PdLnTPVMk/3MYeUaxyOFnlm9f7iNWyiH1rX8lNJ9VOUf7Eps2ysIALavkLRjjSC2zwfOl/RF27+okWElOvV7JekrlAJ6P6UbaUNJn7D90VFn6f0Bk/REYHHzJWh3ypHnCbbvHPUf3y5mqp1rnAeaz5N0lqQ3SJoHfJtyyFbLo5ocu0p65cRLxVwAV0v6nKTdJT1P0meBqytnWkvSsZLOlvT93qVyJuje79X2zZHByyl90VsBr6+YB+BU4H5J2wDHAbOBr9SN1MlMUCHX2B4p2H6bpFcAf9o0HWv7tIqR3koZVHoMD/ZP95h6Yx0AbwT+D3BIc/8HwL/ViwPAKZTuo89RvgV3Qgd/r9aUtCalKHzK9n2SavcZP9AsafMK4F9sHy3p4mQaaOS5xrYoNBYBd9n+D0mPlrS+7btqBLH9I+BHzdK4x9XIsDy2/1fSZ4Dv2F5V+1c8Uktt1y5My9OZ3yvg34EbKZMEfiDpCdTvorxP0muAeTz4BWjNinmgm5mgQq6x7T6S9Gbg65R/NABbAN+sl6j1JUnvkPT15vL25pteNZJeBlxCmSGFpB0l1V699kxJfy1pc0kb9y6VM3Xx9+rTtrew/SKXWSU3UWav1PRG4NmUyQs3SJoNjHp2z+qQCSrkGufZR5dQtv+8wPbTm7bLbf9J5Vyfo3wTOL5pej1wf283ukqZFgLPB87r+1ldZvupFTPdMKDZtrceeZg+Xfu9an5OpwBfsF17HAgASS+hHHU+UDtLTxczQZ1cY3ukANxr+w+9O5LWYMIub5U8w/Y8299vLm+knLxS01Lbv62cYRm2Zw+4VC0Ija79Xj0VuBY4TtLPJB0kqcY5L/32A66V9E+StqucpaeLmaBCrnEuCudLej+wjqQXUr5NnVk5E5SZBk/s3ZG0NfUHUq+Q9FpguqQ5ko4GflIzUNNX/wFJxzb35zTfqmrr1O+V7btsf9b2rsD7gCOAJZKOb2a01Mi0P/B04L+AL0j6aVOs1q+Rp6uZauUa56JwGOWU8cuBt1Cm632gaqLiUOBcSedJOp9yZux7Kmd6O7AD5VT7rwC/5cGZSLV8AfgD5exhKFu5frhenNZDfq9s/22tMJKmS3qZpNOAf6Wcpb41pVB9p1auZprsqcBJwObAK4BFkt6eTJVz2c6lYxfKGidPBZ4GrNWBPPtOpm3EmRY01xf3tV3agZ/VIZNpG2Ge6ynz23cd8NgnK2V6KXAacBnlS9CmTfujgV8kU91cYzfQrLLw3HL/o11p8HRlJ6i50kJ90L3F55rP/wmwJ/Bj2zs1XW5ftb1LrUxNrkE/q4vdDDpXyLOe7d/X+OzlkXQC8DnbPxjw2J625ydT+9kjzzWOReEJK3rclZZOkPSF5mZvQbX5lMXn9qDM+hn5Wc2S/hx4EfBq4Gt9D21AOVO22h/gpr/+A8D2wNnAbsAbbJ9XKc9rgNdSFgn8Yd9D61Nmj72gUq61gQMp3X9r99ptv6lGnui+sTt5rdYf/ZVxmWXUW1Bte09YUK1SrJuBBcDLKOvm9NwFvKtKItqF3TYCXgk8i1I8D7H961qZKAPvS4BNeHB1WSg/q5EtTz3Al4CfA3sBH6KcNV91aqqkZwFHA9sBj6Ksbnu366wE3NlMtXKN45HCXTzYfaTm2tRdorol6QrbT+m7Pw24rL+tQqY1KV8gtnJHzmiW9APbf7ryZ463XtdV77yS5v/lWbafXzHTAspUy1MoS7IfAGzjugPynctUK9c4HilUnWI2CedJOgv4KqVY7UfdBdUA9gY+RvmmMltlhdQP2X5ZxUznSHovpVvr7l6j7d/UCDPhy8YyD1H3y0ZvJc07VZY8/xUwq1KWlu3rJE23fT9lqmXVKc5dzQSjzzV2RaGfpOcAc2x/QdImwPq2B50pOzLu3oJqAEdSztI9D8D2JZJm1YsDQK9P/OC+NlOmW45ch79sHCtpI+D/AmcA6wF/VzcS90h6FHCJpH+idLutm0wDjTzX2HUf9Ug6gnI49mTbT5L0eOAU29X3+W0Gw+e4WVANmO56C6oh6QLbz+yfRVNzmYumS21f219b6ZNHTNLA3cxs3zTqLF3V/H7fSlnO5V3AhsAxtq9Lpvq5xrkoXEI5U3BRF/7Q9eV6M3AQsLHtJ0qaA3zG9p4VMx1HmQ11GPAXwDuANW2/tWKmTo4pNFOee9amrH9/je0dRpzj3St63PYnRpUlVi/j3H30B9tWs7a8pC4cKkLpDtmFsgcytq9V2U+3prcDf0s5o/mrwFnA31dN1LExhb7PX2bhO0k7Uc5sHrVed9aTKWtn9Va1fSllP4yR6+I5Ql3MBHVzjXNROFnSvwOPab6dvwn4bOVM0CyoJpWJUaq/oBq276EUhaozMSbo1JjC8theJGnkCxra/iCApLOBnXrdj5KOpMxkqaG3NlXv/9mXmuvXAfeMPg7QzUxQMdfYdh9BewLUn1FmiJxl+5zKkWgGk+6kTD17O/DXwFU1psZJOpMVf1upOfuokyZ020yj7Lv9WNt7Vcrzc+Bptu9t7q9FWQ5k2xp5mgw/njh2N6ht3DMtL8Owc43tkYLKZhU/7BUCSetImmX7xrrJOIxyBmr/Qn2fq5TlY5U+d6UkHTCo3fYJo84yQf8spKWUPZpPrZQFyjfMC5sF8UxZTO34Fb9k6NaV9ByX3QaRtCv1Z/p0MRNUyDW2RwrNSSG7uln7vpn29WPbtfcu6KTm57Mt5Q/LNe7bM6BSnqP77q5NWQdpke1XVYrUWc24xnObuz+wXXXvYUk7A5+nzKQxZdXdN9lelEz1c41zUbjE9o4T2i61/bRamZoMgwaYfktZbuLDtm+vkOnFwGcoa7qLMqPmLba/O+osyyNpQ+BLtbu0JM2g7Fswca2hamcQd5XKZj9yhzZw6mImGG2use0+Am6T9DLbZwBI2geouXZOz3cpm+p8pbm/X3P9O+CLPLh59yh9HNijNzdaZUXSb1OydsU9wJzaIYATKTOiXgK8lbLh+m1VE3WUyz4BndLFTDDaXONcFN4KnCjpU5Rvv7+kDO7WttuEQaTLewNLkvavlOnWCSfLXE85oaaaCYPg0yirpZ5cL1HrsbaPk3SI7fMpO7GdXztUxGSNbVGw/V/AsyStRzksq3bG8ATrSXqm7QsAJO1CWZoAysBlDVdK+g7lj66BfYGL1OwB4RHu9aCyheRmLDsIvpSyeuR/jyrHCvTWGlrSdLvdDMysmCfiYRnnMYW1KGfnzqKvONr+UK1MAJLmUraa7BWCuyizka4CXmx75N+G9eBeD4PYI1ybX2Vp8ffbvmxC+1zgCNs1utf6c7yEsp/ClpQljzcAPtjrphxhjjfZ/nxzeyZlxtHOlN+jN9j+z1Hm6cu1IWWBxS0oXzBupkwHv7NGnq5mqplrnIvC9ygDuAspffgA2P74cl80/EzTgXfY/ufmF0K1fzG7RhOWFp/w2OUTzygeFUn/aPtvJO1ru9bJYf152h3gJJ1MWabks8A+wNtqLJvSTCM+grIpUu+obibwQkrhHPl04i5mqp1rnIvCcv+41CTpPNu7187Rrzmn4+089Khq5DN9JF1ne5uH+9iwNbPGdgIucMVtSvvy9BeFZWbaqdL2oJKuAZ458YuOyiquF9h+UjLVzzW2YwrATyT9ie3LV/7UkfpxM/g9cU2fmvOlv0nZ/P1M4IGKOaCMZbzZ9jJLkkg6kGV3hxu171Fmr60r6Xc0+yj0rj36/RRmSvpk8/kzJK1puzfeseaIs/T0fiYTPdA8VkMXM0HFXONcFJ4DvEHSDZSF3nr/eKuukkrZnxnK1ok9BmrOc/9f25+s+Pn93gmcJul1PFgE5lI2AHpFrVC2DwUOlXS67X1q5ehzaN/tBZQxqjskPY4HF8cbtaOARc16TL9s2raidInUWmCxi5mgYq5x7j56wqB2d3QP55okvZZyDsDZlAIK1D16kbQH0Ov+u9L292tlmUjL7oexDrBGh2a3VdV0f+xFGTwVsJgyeHpHMnUj19gWhR6VZan7zzytvhlKM5Vx4hmx1WZFSfp/wOspZzT3uo+cs3QfSh3cD2MiSX9Xe5ZdVzVnDs8Brq9dFHqa4rB0VF8spo3iQ7pI0sskXQvcAJwP3EgHztCV9BngLykDu6KcEzDwqGaEXgFsbft5tvdoLikIgx0M7EY5Ax3b1wK198OY6K9qfKikbSV9V9K3JT1R0hcl3SnpQknbVcr0ZZWteJG0F3Al8I+U7S/3rZGpyfJ4SSdI+i1lrOpKSTdJOlLSUMeExrYoUPrlngX8p+3ZlAXVflw3ElAW6TsAuMNlTfxnU+a813Qp8JjKGVYX9/YvFqhK+2FI+t1yLncBjx91nsaxwDHAl4HvUwbnN6L8W/xUpUxPs91b3uYI4Lm2X0A5p+MDlTJB+Rl93vaGlC+GpwLbUcaBPz3MDx7nonBfs7jcNEnTbJ8L7LiyF43A/zTX96jsG30fZQG6mjYDfi7pLEln9C6VM3XV+ZLeD6yjsl/HKZRZW6N2J2VcY4MJl/Upm7/XsL7tM21/lfLv7yQXZ1KKQw3Tmi4jKF2jNwE0haLmRJzH2j6vyfIN4E9t3237A8BQt6Ed59lHdzZLXPyAsgbSrdRbRqLftyQ9BvgosIjyLbPWfgo9R1T+/NVJV/bDOIHS7XjLgMe+MqBtFKb33Z64R/SjRhmkzweBcyV9mtJTcIqk0ymz/b5XKROUBTv3pxxR/QWlextJYshf5sduoLlv7ZxLKN/Kp1G2uHsC8G3bNee6L6NZimPtLizjK2kzyl6/ABfarrogXpepLJ+N7ayO2kfSW4ATbf9+Qvs2lLOs31kp1zbAm4EnUb4oLwa+afusGnmaTFtR1vfanvK36lDbSyQ9Ftjd9tA2bhrHotDptXOaLLvy0LOHq+0oJunVlCOX8yiD38+l/JJ+vVamrmm+wR0BvI3yMxJl+ZSjuzLTR9KRto+snSO6bRyLQifXzunL8CXgiZRvB701mWz7HRUzXQq8sHd00HwT/g9X3pCoSyS9C3gRcJDtG5q2rYF/A75n+59r5mvytEtfdEUyTd6oco3jmMLaK3hsnZGlWL65wPbuVrWeNqG76HbGe5LCIAdQCme7UZPt65t+4bOB6kWBuss2LE8yTd5Ico3jP+yLmhOMltGBtXN6rgAeVzvEBN9rZh69QdIbKLuufadypq5Zs78g9DTjCrXWGppo59oBBvh27QADdDETjCjXOHYfbQacBvyBAWvn2P5VpVy9ncTWp0yNvZBll5SosSLpNsBmtn+ssqHOcyjfVu6gDBj+16gzddWKDu271h3RlTOau3j2cIxhUejp2to5zdHLZpQNWvo9D/hv28dVyNT5QfmukHQ/fava9j9EmUHWlaMFJN1ke6sKn/tl4J22f92cPfw54BpKYXivO7APRb8ujDEOMuxc4zimAEBzstq5tXP02YfBf4DvpsxqGXlRAGZNzANge4GkWaOP0122p6/8WaPTLN898CHqjZ0NOnv4xmaZifmUE/1GqjkCHvgQFbtxa+Ya26LQQV38A9z1QflYvjuBZ9h+yMlrkn454PmjME3SBrZ/x4Szh5vlQGr4GnAig5ciWdHv/7BVy5Wi0B1d/APc1Q1tYuW6eEZzF88evgz4mO0rJj4g6QUV8vRUyzW2YwpdI+mrwPeX8wf4z2z/ZYVMnRyUj9VX184elvRc4BeDlsyXNNf2ggqxquZKUeiILv8B7tqgfKycpFm2b1zB4wK2sL14dKlidZCi0DH5AxyrgvGnLI8AAAjzSURBVKRTKOchnU75knEbpYtyG2APylLxR9g+p1rIPrWmyTbFcV9K3/3XKV1Z+wA/p2yOVGVPckmvAM63/ZtmBYGPA08HrgLeM8xinqIQMUVJ2p6y2ONuwObAPcDVlBMPv277fyvGW0bFabLHUDZBehRlY6S1KEudvwi4xfYho87U5LrK9vbN7a8BP6PMznoB8DrbLxzaZ6coRMQorGyarO2RT3zpzflvdjP7FbC57T80s6EurnWegqRrbD+5ub3Q9s59j11ie2h7v4zjMhcRUUcXN/5ZCmD7PuAiN7vm2V7KgwtS1nCepA9JWqe5/XJou5eHupR+ikJEjEpvmuwgtabJ/qrZbAvbe/caJT2OMumjlrdRzuW4hjLm8Y1mK9U3A68f5gen+ygiYgJJ6wLrdmEzKUkbAmu4bB88dDlSiJjCVOwv6e+a+1tJ2qVSllkreVySZo4mTfuZswa1N/sh31ojEyyby/ZvJxaEYebKkULEFCbp3yjdEM+3vZ2kjYCzbT9jJS8dRpbOTZPtYqbauVIUIqaw3rLdki62/fSm7dJau+Z1cZpsFzPVzJWiEDGFSboA2JUys2an5kSos3sFImKijClETG2fpCyfsqmko4AfAf9QN1J0WY4UIqY4SdtS+qAFzLd9deVI0WEpChFTmKSBS0cMWn0zAlIUIqY0SZdTFnsTZfbKbOAa2ztUzCTKAOrWtj/UFK7H2b4wmernSlGIGCOSdgLeYvstFTN0ZppslzPVypWd1yLGiO1Fkqr+oQOe2Zsm22S6Q9KjkmmgkedKUYiYwiS9u+/uNGAnyolQNd0naTrN/sPNNNkq+xb06WImqJArU1Ijprb1+y5rAd+mbCJTUxenyXYxE1TIlTGFiBi5Lk6T7WImGH2uFIWIKUrSPOAQ4MlN09XAJ22fUC9VN6fJdjET1MmVMYWIKUjSAcA7gXcDiyjfMncCPiqJyoXh2wyYJgtUmybb0UxQIVeOFCKmIEk/A/azfeOE9lnASbafVSHWQF2YJjtRFzPBaHJloDliatpgYkEAaNo2GHmaFbC9CKg9TXYZXcwEo8mV7qOIqel//sjHhq6L02S7mAnq5EpRiJiatpN02YB2AVuPOswE6/fdXkrpNz+1UpaeLmaCCrkyphAxBUl6wooet/2LUWWJ1UvGFCKmINu/WNGlVi5J8yQtknR3c1nQzJSqpouZauZK91FEjEQXp8l2MVPtXOk+ioiR6OI02S5maj6/Wq50H0VMYZIOmUzbiHRxmmwXM0HFXCkKEVPbvAFtbxh1iEYXp8l2MdPKPnuoudJ9FDEFSXoN8FrgOcAP+x7aAFhq+wUVMt0DXDfoIcrOYuuOOFInM0HdXBlojpiafgIsATYBPt7Xfhcw6PyFUdiu0ueuSBczQcVcOVKImMIkrQv8j+0HJD0J2Bb4ru37KkeLjkpRiJjCJC0EngtsBPwMWADcY/t1VYNFZ2WgOWJqk+17gFcCR9t+BbB95UzRYSkKEVObJD0beB1l3RyoPJbYsWmyy/382pmWl2HYuVIUIqa2Q4DDgdNsXylpa+Dcypm6NE22p4uZoEKujClExEh0dJps5zLVzpUpqRFTmKQZwPso2zeu3Wu3/fwKcbo4TbaLmaBirhwpRExhks4Gvga8F3grpTviNtt/UzFT56bJdjFTrVwpChFTmKSFtneWdJntpzZt59t+Xs1MdGyabBcz1cqVgeaIqa33jXKJpBdLejows2YgujlNtouZoEKujClETG0flrQh8B7gaMpA5bvqRlpmmuyBTVvtv0VdzAQVcnXhPzoiVjFJa1PGELYBtgCOs71H3VStLk6T7WImqJArYwoRU5Ckr1G6jn4I/DnwC9vVT8aK7ktRiJiCJF1u+0+a22sAF9reqXIsoHPTZDubCerkykBzxNTUTlm0vbRmkAFOBH4OzAY+CNwIXFQzEN3MBBVy5UghYgqSdD9wd+8usA5wT3PbtqttNdnVabJdy1QrVwaaI6Yg29NrZ1iBZabJAjdTf5psFzNBhVwpChExal2cJtvFTFAhV7qPImIkJkyTvZwyTbbqeEcXM0HdXCkKETESXZwm28VMUDdXikJEjEQXp8l2MVOTpVquTEmNiFHp4jTZLmaCirlypBARI9HFabJdzFQ7V4pCRES00n0UERGtFIWIiGilKERERCtFIcaKpN8P8b2nSfqkpCskXS7pIkmzm8feP8n3mNTzIoYlA80xViT93vZ6Q3rv1wB/Aby62Wh9JnC37Tsm+7nDzBcxGTlSiLEnaUdJP5N0maTTJG3UtL+5+bZ/qaRTJT26af9ic0TwE0nXS3pV81abA0tsPwBge3FTED4CrCPpEkknNu/xTUkLJV0p6aCmbZnnSZol6Yq+nO+VdGRz+x2SrmoynzSiH1WMgRwpxFgZ9E1c0mXA222fL+lDwAa23ynpsbZvb57zYeAW20dL+iKwLvCXwLbAGba3aY4MfgTcCcwHvmz74kGfK2lj27+RtA5lffzn2b69/3mSZgHfsv2U5v57gfVsHynpZmC27XslPcb2nUP6kcWYyZFCjLVmBcrH2D6/aToe+NPm9lMk/VDS5ZSN03foe+k3bT9g+ypgMyhHBsCTKXvqPgDMl7Tncj76HZIuBX4GbAnMeZjRLwNOlLQ/0KUzcWM1l6IQsXxfBN7WrEHzQfq2QwTu7but3g3b99r+ru1DgX8AXj7xTSXtDrwAeLbtpwEXT3jvnqUs+2+0/zkvBj4N7AwsbNbHiXjEUhRirNn+LXCHpOc2Ta8HekcN61M2N1mTcqSwQpJ2kvT45vY04KnAL5qH72veB2BD4A7b90jaFnhW39v0P+8WYFNJj5W0FvCSvvfe0va5lP17HwNkcDpWiXy7iHHzaEmL++5/ApgHfKYZSL4eeGPz2P8FLqD8Yb+cUiRWZFPgs80fcIALgU81t48FLpO0CHgT8NZmLOMaShcSE59n+3XNGMcFwA2UvXoBpgNfbrq+BPxzxhRiVclAc0REtNJ9FBERrRSFiIhopShEREQrRSEiIlopChER0UpRiIiIVopCRES0/j+dqhNGAt0gJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting color\n",
    "base_color = sns.color_palette()[0]\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(data = df, x = 'LoanStatus', color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "* Most of the loans in the data set are actually current loans. \n",
    "* Past due loans are split in several groups based on the length of payment delay.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "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": [
    "sns.countplot(data = df, x = 'EmploymentStatus', color = base_color);\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* The majority of borrowers are employed and all other categories as small part of borrowers. \n",
    "* In small Group full time has highest, after that self empolyed are there and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stated Monthly Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "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='StatedMonthlyIncome', bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**Note**: Distribution of stated monthly income is highly skewed to the right. so, we have to check how many outliers are there)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_std = df['StatedMonthlyIncome'].std()\n",
    "income_mean = df['StatedMonthlyIncome'].mean()\n",
    "boundary = income_mean + income_std * 3\n",
    "len(df[df['StatedMonthlyIncome'] >= boundary])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "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='StatedMonthlyIncome', bins=100);\n",
    "plt.xlim(0, boundary);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* With a boundary of mean and 3 times standard deviations distribution of monthly income.\n",
    "* But still has noticeable right skew but now we can see that mode is about 5000.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 class=\"list-group-item list-group-item-action active\" >4. BIVARIATE ANALYSIS</h4>\n",
    "\n",
    ">#### A. Check the correlation matrix for all numeric variables. Maintain the Strong positive and Negative correlations columns.\n",
    ">#### B. Check the relation between `LoanOriginalAmount` and `BorrowerAPR` columns. \n",
    "\n",
    "    Step1: Use subplots. \n",
    "    \n",
    "        Plot1: Scatter Plot of LoanOriginalAmount and BorrowerAPR columns    \n",
    "\n",
    "        Plot2: HeatMap of LoanOriginalAmount and BorrowerAPR\n",
    "        \n",
    "    Step2: Write your observations.         \n",
    "        \n",
    ">#### C.Display the seperate box plot for `y = BorrowerAPR`  with x1 = `LoanStatus`, x2 = `EmploymentStatus` columns.Write your observations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Check the correlation matrix for all numeric variables. Maintain the Strong positive and Negative correlations columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix for all numeric variables\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.heatmap(df.corr(), annot = True, fmt = '.3f',\n",
    "           cmap = 'vlag_r', center = 0)\n",
    "plt.title('Correlation Matrix') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Strong positive correlations between Lender yield and Borrower APR. Credit Score upper range has also some weak +ve correlation with prosper score.\n",
    "\n",
    "* Negative correlation between prosper score & APR, and prosper score & Lender yield."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Check the relation between `LoanOriginalAmount` and `BorrowerAPR` columns. \n",
    "\n",
    "    Step1: Use subplots. \n",
    "    \n",
    "        Plot1: Scatter Plot of LoanOriginalAmount and BorrowerAPR columns    \n",
    "\n",
    "        Plot2: HeatMap of LoanOriginalAmount and BorrowerAPR\n",
    "        \n",
    "    Step2: Write your observations.         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the relation between LoanOriginalAmount and BorrowerAPR\n",
    "\n",
    "plt.figure(figsize = [15, 6]) \n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(data = df, x = 'LoanOriginalAmount', y = 'BorrowerAPR',  alpha =  0.01)\n",
    "plt.title('Scatter Plot of LoanOriginalAmount and BorrowerAPR')\n",
    "plt.xlabel('LoanOriginalAmount')\n",
    "plt.ylabel('BorrowerAPR');\n",
    "plt.colorbar();\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "bins_x = np.arange(df['LoanOriginalAmount'].min(), df['LoanOriginalAmount'].max()+1000, 1000)\n",
    "bins_y = np.arange(df['BorrowerAPR'].min(), df['BorrowerAPR'].max()+0.1, 0.1)\n",
    "plt.hist2d(data = df, x = 'LoanOriginalAmount', y = 'BorrowerAPR', bins = [bins_x, bins_y], \n",
    "               cmap = 'viridis_r', cmin = 0.5)\n",
    "plt.title('HeatMap of LoanOriginalAmount and BorrowerAPR')\n",
    "plt.xlabel('LoanOriginalAmount')\n",
    "plt.ylabel('BorrowerAPR');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see negative relationship between loan original amount and borrower apr, means large loans have relatively less annual interest rate from the borrower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C.Display the separate box plot for `y = BorrowerAPR`  with x1 = `LoanStatus`, x2 = `EmploymentStatus` columns.Write your observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Borrower APR vs Status of Loan \n",
    "plt.figure(figsize = [20, 5])\n",
    "sns.boxplot(data=df,y='BorrowerAPR',x='LoanStatus',color=base_color);\n",
    "plt.title('BorrowerAPR & LoanStatus');\n",
    "plt.ylabel('Borrower APR');\n",
    "plt.xlabel('Loan Status');\n",
    "plt.xticks(rotation=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Borrower APR vs Employment status\n",
    "plt.figure(figsize = [20, 5])\n",
    "\n",
    "sns.boxplot(data=df,y='BorrowerAPR',x='EmploymentStatus',color=base_color);\n",
    "plt.title('BorrowerAPR & EmploymentStatus');\n",
    "plt.ylabel('Borrower APR');\n",
    "plt.xlabel('Employment Status');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* On BorrowerAPR vs LoanStatus graph we notice that Current and completed loans have lower rate than the past-due loans and have lower mean.\n",
    "* On Borrower APR vs Employment status graph we notice that Employed people have lower IQR than not employed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 class=\"list-group-item list-group-item-action active\" >5.  MULTI VARIATE ANALYSIS, FEATURE ENGINEERING</h4>\n",
    "\n",
    ">#### A. Write a program\n",
    "\n",
    "    Step 1: Create a  condition = 'LoanStatus'== 'Completed'| 'LoanStatus' == 'Defaulted' |\\'LoanStatus' == 'Chargedoff'\n",
    "\n",
    "    Step2: Create a user define function  using condition and `LoanStatus` column.\n",
    "\n",
    "    Hint: df['LoanStatus'] = df.apply(user define function , axis=1)\n",
    "    \n",
    "        Sample output : \n",
    "        \n",
    "        \n",
    "        Completed    168\n",
    "        \n",
    "        Defaulted     59\n",
    "\n",
    "\n",
    ">#### B. Write a program\n",
    "\n",
    "    Step 1: Create a  dictionary called categories = 1: 'Debt Consolidation', 2: 'Home Improvement', 3: 'Business', 6: 'Auto', 7: 'Other'\n",
    "\n",
    "    Step2: Create a user define function  using categories and `ListingCategory (numeric)` column.\n",
    "\n",
    "    Hint: df['ListingCategory (numeric)'] = df.apply(user define function , axis=1)\n",
    "    \n",
    "        Sample output : \n",
    "        \n",
    "        \n",
    "        Debt Consolidation    106\n",
    "\n",
    "        Other                  65\n",
    "\n",
    "        Business               25\n",
    "\n",
    "        Home Improvement       22\n",
    "\n",
    "        Auto                    9\n",
    "\n",
    ">#### C. Display the box plot for `ProsperRating (Alpha)` vs `LoanOriginalAmount` and hue = `Loan Status`[Completed,Defaulted]. Write your observations.\n",
    "\n",
    ">#### D. Display the catplot for `ProsperRating (Alpha)` vs `ListingCategory (numeric)`[Debt Consolidation,Other,Business,Home Improvement,Auto] and hue = `Loan Status`[Completed,Defaulted]. Write your observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Write a program\n",
    "\n",
    "    Step 1: Create a  condition = 'LoanStatus'== 'Completed'| 'LoanStatus' == 'Defaulted' |\\'LoanStatus' == 'Chargedoff'\n",
    "\n",
    "    Step2: Create a user define funcation using condition and `LoanStatus` column.\n",
    "\n",
    "    Hint: df['LoanStatus'] = df.apply(user define funcation, axis=1)\n",
    "    \n",
    "        Sample output : \n",
    "        \n",
    "        \n",
    "        Completed    168\n",
    "        \n",
    "        Defaulted     59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Completed    168\n",
       "Defaulted     59\n",
       "Name: LoanStatus, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "condition = (df['LoanStatus'] == 'Completed') | (df['LoanStatus'] == 'Defaulted') |\\\n",
    "                  (df['LoanStatus'] == 'Chargedoff')\n",
    "df = df[condition]\n",
    "\n",
    "def change_to_defaulted(row):\n",
    "    if row['LoanStatus'] == 'Chargedoff':\n",
    "        return 'Defaulted'\n",
    "    else:\n",
    "        return row['LoanStatus']\n",
    "    \n",
    "df['LoanStatus'] = df.apply(change_to_defaulted, axis=1)\n",
    "df['LoanStatus'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* After transforming dataset we have 168 completed loans and 59 defaulted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Write a program\n",
    "\n",
    "    Step 1: Create a  dictionary called categories = 1: 'Debt Consolidation', 2: 'Home Improvement', 3: 'Business', 6: 'Auto', 7: 'Other'\n",
    "\n",
    "    Step2: Create a user define funcation using categories and `ListingCategory (numeric)` column.\n",
    "\n",
    "    Hint: df['ListingCategory (numeric)'] = df.apply(user define funcation, axis=1)\n",
    "    \n",
    "        Sample output : \n",
    "        \n",
    "        \n",
    "        Debt Consolidation    106\n",
    "\n",
    "        Other                  65\n",
    "\n",
    "        Business               25\n",
    "\n",
    "        Home Improvement       22\n",
    "\n",
    "        Auto                    9\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Debt Consolidation    106\n",
       "Other                  65\n",
       "Business               25\n",
       "Home Improvement       22\n",
       "Auto                    9\n",
       "Name: ListingCategory (numeric), dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = {1: 'Debt Consolidation', 2: 'Home Improvement', 3: 'Business', 6: 'Auto', 7: 'Other'}\n",
    "def reduce_categorie(row):\n",
    "    loan_category = row['ListingCategory (numeric)']\n",
    "    if  loan_category in categories:\n",
    "        return categories[loan_category]\n",
    "    else:\n",
    "        return categories[7]\n",
    "    \n",
    "df['ListingCategory (numeric)'] = df.apply(reduce_categorie, axis=1)\n",
    "df['ListingCategory (numeric)'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable Listing Category is set up as numeric and most of the values have very `low frequency`, for the easier visualization so we have change it to `categorical and reduce the number of categories`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Display the box plot for `ProsperRating (Alpha)` vs `LoanOriginalAmount` and hue = `Loan Status`[Completed,Defaulted]. Write your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 8])\n",
    "sns.boxplot(data=df, x='ProsperRating (Alpha)', y='LoanOriginalAmount', hue='LoanStatus');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* Except for the lowest ratings defaulted credits tend to be larger than completed.\n",
    "* Most of the defaulted credits comes from individuals with low Prosper rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Display the catplot for `ProsperRating (Alpha)` vs `ListingCategory (numeric)`[Debt Consolidation,Other,Business,Home Improvement,Auto] and hue = `Loan Status`[Completed,Defaulted]. Write your observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170.12x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'ProsperRating (Alpha)', hue = 'LoanStatus', col = 'ListingCategory (numeric)',\n",
    "              data = df, kind = 'count', palette = 'Blues', col_wrap = 3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* There are 5 graphs in the second one has much up and downs in it other than all of them.\n",
    "* There is no substantial difference for default rates in different categories broken up by ratings."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}