web_scraping/Lending_Data_Analysis.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get data from <a href=\"http://www.lendingclub.com\">Lending Club</a>.  We'll try to create a scatter plot of loan interest rate versus FICO score to see if there is a relationship between the two."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's take a look at the data.  We'll take a peek at the first 5 records using Panda's dataframe's head() method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Amount.Requested</th>\n",
        "      <th>Amount.Funded.By.Investors</th>\n",
        "      <th>Interest.Rate</th>\n",
        "      <th>Loan.Length</th>\n",
        "      <th>Loan.Purpose</th>\n",
        "      <th>Debt.To.Income.Ratio</th>\n",
        "      <th>State</th>\n",
        "      <th>Home.Ownership</th>\n",
        "      <th>Monthly.Income</th>\n",
        "      <th>FICO.Range</th>\n",
        "      <th>Open.CREDIT.Lines</th>\n",
        "      <th>Revolving.CREDIT.Balance</th>\n",
        "      <th>Inquiries.in.the.Last.6.Months</th>\n",
        "      <th>Employment.Length</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>81174</th>\n",
        "      <td> 20000</td>\n",
        "      <td> 20000</td>\n",
        "      <td>  8.90%</td>\n",
        "      <td> 36 months</td>\n",
        "      <td> debt_consolidation</td>\n",
        "      <td> 14.90%</td>\n",
        "      <td> SC</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td>  6541.67</td>\n",
        "      <td> 735-739</td>\n",
        "      <td> 14</td>\n",
        "      <td> 14272</td>\n",
        "      <td> 2</td>\n",
        "      <td> &lt; 1 year</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99592</th>\n",
        "      <td> 19200</td>\n",
        "      <td> 19200</td>\n",
        "      <td> 12.12%</td>\n",
        "      <td> 36 months</td>\n",
        "      <td> debt_consolidation</td>\n",
        "      <td> 28.36%</td>\n",
        "      <td> TX</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td>  4583.33</td>\n",
        "      <td> 715-719</td>\n",
        "      <td> 12</td>\n",
        "      <td> 11140</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2 years</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80059</th>\n",
        "      <td> 35000</td>\n",
        "      <td> 35000</td>\n",
        "      <td> 21.98%</td>\n",
        "      <td> 60 months</td>\n",
        "      <td> debt_consolidation</td>\n",
        "      <td> 23.81%</td>\n",
        "      <td> CA</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> 11500.00</td>\n",
        "      <td> 690-694</td>\n",
        "      <td> 14</td>\n",
        "      <td> 21977</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2 years</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15825</th>\n",
        "      <td> 10000</td>\n",
        "      <td>  9975</td>\n",
        "      <td>  9.99%</td>\n",
        "      <td> 36 months</td>\n",
        "      <td> debt_consolidation</td>\n",
        "      <td> 14.30%</td>\n",
        "      <td> KS</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td>  3833.33</td>\n",
        "      <td> 695-699</td>\n",
        "      <td> 10</td>\n",
        "      <td>  9346</td>\n",
        "      <td> 0</td>\n",
        "      <td>  5 years</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33182</th>\n",
        "      <td> 12000</td>\n",
        "      <td> 12000</td>\n",
        "      <td> 11.71%</td>\n",
        "      <td> 36 months</td>\n",
        "      <td>        credit_card</td>\n",
        "      <td> 18.78%</td>\n",
        "      <td> NJ</td>\n",
        "      <td>     RENT</td>\n",
        "      <td>  3195.00</td>\n",
        "      <td> 695-699</td>\n",
        "      <td> 11</td>\n",
        "      <td> 14469</td>\n",
        "      <td> 0</td>\n",
        "      <td>  9 years</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       Amount.Requested  Amount.Funded.By.Investors Interest.Rate Loan.Length  \\\n",
        "81174             20000                       20000         8.90%   36 months   \n",
        "99592             19200                       19200        12.12%   36 months   \n",
        "80059             35000                       35000        21.98%   60 months   \n",
        "15825             10000                        9975         9.99%   36 months   \n",
        "33182             12000                       12000        11.71%   36 months   \n",
        "\n",
        "             Loan.Purpose Debt.To.Income.Ratio State Home.Ownership  \\\n",
        "81174  debt_consolidation               14.90%    SC       MORTGAGE   \n",
        "99592  debt_consolidation               28.36%    TX       MORTGAGE   \n",
        "80059  debt_consolidation               23.81%    CA       MORTGAGE   \n",
        "15825  debt_consolidation               14.30%    KS       MORTGAGE   \n",
        "33182         credit_card               18.78%    NJ           RENT   \n",
        "\n",
        "       Monthly.Income FICO.Range  Open.CREDIT.Lines  Revolving.CREDIT.Balance  \\\n",
        "81174         6541.67    735-739                 14                     14272   \n",
        "99592         4583.33    715-719                 12                     11140   \n",
        "80059        11500.00    690-694                 14                     21977   \n",
        "15825         3833.33    695-699                 10                      9346   \n",
        "33182         3195.00    695-699                 11                     14469   \n",
        "\n",
        "       Inquiries.in.the.Last.6.Months Employment.Length  \n",
        "81174                               2          < 1 year  \n",
        "99592                               1           2 years  \n",
        "80059                               1           2 years  \n",
        "15825                               0           5 years  \n",
        "33182                               0           9 years  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The problem I see immediately is that the interest rate has the % symbol at the end.  This will pose a problem when plotting since the value will be read or viewed as a string/text instead of a decimal number.  There is also a problem with the FICO score.  It is a range instead of a single value.  Since the ranges seem pretty tight, let's just use the first value in the range."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Now, let's remove the percent symbol from the interest rate and replace it with a \"blank\" space.  Then read it as a float type value:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "irate = [float(rate.replace('%','')) for rate in loansData[\"Interest.Rate\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I used Python's list comprehension to create a Python list containing interest rates without the percent symbol.  Now, let's see if I successfully did that. Let's look at the first 50 values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(irate[:50])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[8.9, 12.12, 21.98, 9.99, 11.71, 15.31, 7.9, 17.14, 14.33, 6.91, 19.72, 14.27, 21.67, 8.9, 7.62, 15.65, 12.12, 10.37, 9.76, 9.99, 21.98, 19.05, 17.99, 11.99, 16.82, 7.9, 14.42, 15.31, 8.59, 7.9, 21.0, 12.12, 16.49, 15.8, 13.55, 7.9, 7.9, 7.62, 8.9, 12.49, 17.27, 11.14, 19.13, 21.74, 17.27, 11.86, 10.38, 23.91, 7.49, 12.12]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Looks like it worked!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Now, let's get the first or lowest value of the FICO score range:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fico_score = [int(fico.split('-')[0]) for fico in loansData[\"FICO.Range\"]]\n",
      "print(fico_score[:50])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[735, 715, 690, 695, 695, 670, 720, 705, 685, 715, 670, 665, 670, 735, 725, 730, 695, 740, 730, 760, 665, 695, 665, 695, 670, 705, 675, 675, 765, 760, 685, 685, 720, 685, 675, 780, 720, 830, 715, 660, 670, 720, 660, 660, 675, 715, 710, 670, 785, 705]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Now that we have \"cleaned up\" our data, we can now do a scatter plot of interest rate vs FICO score:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(fico_score, irate)\n",
      "title(\"Loan Interest Rate vs FICO Score\", weight=\"bold\")\n",
      "ylabel(\"Interest Rate (%)\", weight=\"bold\")\n",
      "xlabel(\"FICO Score\", weight=\"bold\")\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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UDldUChUqVMCzzz6LlJSUgPN16tRBx44dsXfvXuzZs6fMBRSUnJwcHVat4hFH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LG2BDMzD/DHH9Z7v/5qZ926Hcc839n0+59FMcov2ytgzpw5TJ8+nYKCAtq3b8+qVau47rrrGDRo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\nEOKVV5y88IKLDRtUUyNK04yIb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jF9+nRGjBhBeno6v/76K1dccQWDBg0CoFGjRnz99dcA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\niUQikZhIpyCRSCQSk/8H95NDOg4CXF0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x51b37f0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(irate)\n",
      "title(\"Loan Interest Rate Histogram\", weight=\"bold\")\n",
      "xlabel(\"Interest Rate %\")\n",
      "ylabel(\"Frequency/Qty\")\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
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7Ng4fPgwAyMvLw/z58zF37lzNxfiaayciIvfStcWQk5ODzMxM9O3bF+fPn0dU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\nuQSi/QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x50f58b0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(fico_score)\n",
      "title(\"Histogram of FICO Score\", weight=\"bold\")\n",
      "xlabel(\"FICO Score\")\n",
      "ylabel(\"Frequency/Qty\")\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Yvnx5p8vW1NQgICAAAJCdnY05c+YgMzMT8+fPhxACWVlZSElJAQDV8o5yc3MR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\nmFsE0AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5020a50>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Let's see what the breakdown of the loan purposes were:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = loansData.groupby(\"Loan.Purpose\")[\"Loan.Purpose\"].count()\n",
      "grouped.sort(ascending=False)\n",
      "grouped"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "Loan.Purpose\n",
        "debt_consolidation    1307\n",
        "credit_card            444\n",
        "other                  201\n",
        "home_improvement       152\n",
        "major_purchase         101\n",
        "small_business          87\n",
        "car                     50\n",
        "wedding                 39\n",
        "medical                 30\n",
        "moving                  29\n",
        "vacation                21\n",
        "house                   20\n",
        "educational             15\n",
        "renewable_energy         4\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Looks like most loans were for debt consolidation and credit card debt."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}