Oleh_Dubno_Final_Loans

Oleh_Dubno_Final_Loans

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Loan Data (2007-2011) From Lending Club"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####We will be going over two things:\n",
      "\n",
      "\u2022 Logistic regression, to determine which features of the data set contribute towards someone paying off their loan or defaulting on their loan in United States.\n",
      "\n",
      "\u2022 Using cartodb to map the features of the data set and see which states stand out among the rest in terms of paying back their loans off."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Here's the data set:\n",
      "https://www.lendingclub.com/info/download-data.action"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Cleaning the Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from datetime import datetime\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1022
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = '/Users/olehdubno/Desktop/python_tests/LoanStats3b2.csv'\n",
      "loan = pd.read_csv(url, low_memory = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1023
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creating a separate set of features we will be cleaning and working with."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2 = loan[['funded_amnt','emp_length','annual_inc','loan_status','home_ownership','addr_state','tax_liens','grade']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1024
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.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>funded_amnt</th>\n",
        "      <th>emp_length</th>\n",
        "      <th>annual_inc</th>\n",
        "      <th>loan_status</th>\n",
        "      <th>home_ownership</th>\n",
        "      <th>addr_state</th>\n",
        "      <th>tax_liens</th>\n",
        "      <th>grade</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 24000</td>\n",
        "      <td> 10+ years</td>\n",
        "      <td> 100000</td>\n",
        "      <td>    Current</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> MI</td>\n",
        "      <td> 0</td>\n",
        "      <td> B</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 11100</td>\n",
        "      <td> 10+ years</td>\n",
        "      <td>  90000</td>\n",
        "      <td>    Current</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> NY</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12000</td>\n",
        "      <td>   3 years</td>\n",
        "      <td>  96500</td>\n",
        "      <td>    Current</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> TX</td>\n",
        "      <td> 0</td>\n",
        "      <td> A</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 15000</td>\n",
        "      <td> 10+ years</td>\n",
        "      <td>  98000</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td>     RENT</td>\n",
        "      <td> NY</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 27600</td>\n",
        "      <td>   6 years</td>\n",
        "      <td>  73000</td>\n",
        "      <td>    Current</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> CO</td>\n",
        "      <td> 0</td>\n",
        "      <td> D</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1025,
       "text": [
        "   funded_amnt emp_length  annual_inc loan_status home_ownership addr_state  \\\n",
        "0        24000  10+ years      100000     Current       MORTGAGE         MI   \n",
        "1        11100  10+ years       90000     Current       MORTGAGE         NY   \n",
        "2        12000    3 years       96500     Current       MORTGAGE         TX   \n",
        "3        15000  10+ years       98000  Fully Paid           RENT         NY   \n",
        "4        27600    6 years       73000     Current       MORTGAGE         CO   \n",
        "\n",
        "   tax_liens grade  \n",
        "0          0     B  \n",
        "1          0     C  \n",
        "2          0     A  \n",
        "3          0     C  \n",
        "4          0     D  "
       ]
      }
     ],
     "prompt_number": 1025
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cleaning:\n",
      "\n",
      "\u2022Convert \"loan_status\" to booleans\n",
      "\n",
      "\u2022Clean up \"emp_length\"\n",
      "\n",
      "\u2022Convert \"grade\" to integer values\n",
      "\n",
      "\u2022Drop N/A values from fields\n",
      "\n",
      "\u2022Account for outliers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dropping N/A values (It's only 4 rows and not very significant)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.dropna().info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 188123 entries, 0 to 188122\n",
        "Data columns (total 8 columns):\n",
        "funded_amnt       188123 non-null float64\n",
        "emp_length        188123 non-null object\n",
        "annual_inc        188123 non-null float64\n",
        "loan_status       188123 non-null object\n",
        "home_ownership    188123 non-null object\n",
        "addr_state        188123 non-null object\n",
        "tax_liens         188123 non-null float64\n",
        "grade             188123 non-null object\n",
        "dtypes: float64(3), object(5)"
       ]
      }
     ],
     "prompt_number": 1026
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Data Overview"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets plot Annual Income against Funded Amount."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10,5))\n",
      "plt.scatter(loan_2['annual_inc'], loan_2['funded_amnt'])\n",
      "plt.title(\"Plotting Annual Income against Funded Amount\")\n",
      "plt.ylabel('Funded Amount')\n",
      "plt.xlabel('Annual Income')\n",
      "plt.show()\n",
      "\n",
      "loan_2.annual_inc.hist(figsize=(10,5))\n",
      "plt.ylabel('Number of Loans')\n",
      "plt.xlabel('Annual Income')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6Ae8+7haKgE9kKH8FW7VEsW7iHsAlwN+B/8Dydm4kvhZycfE0rrnmMioqFlFR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\nsKameNO1NTXnpy334rkCVBERkd2joLBkNRKjgg8Qv+YuRMW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tFP4/blML7Y0U/uM4N81b\nDXwSOMHd3zazB3PndlHz+3ina63Z89yIZfZ9mjLPm8j3wYDjU+AoIrLbNFIoIp3J54Fb3H2Auw90\n9/cCK8zso7v5fiuB4wDSNPTA1L4v8PcUEB4JnLCH923u/g9gtZl9Nn1eDzOrBP4bOMPMKszsAOBj\nwEK2DxR35D5g/LYPMjt2D+9VRMqUgkIR6UzOBOY2a7uLmEJ2di0DOPu6u4AqM3sS+BrwXGq/F+hq\nZk8DM4gp5F1535aOs8/PIdYnLgUeBg5097nAE8BS4AHg4jSNvLP+ZM+NB4aa2VIzewo4fxfuVURk\nOxZLb0RERESknGmkUEREREQUFIqIiIiIgkIRERERQUGhiIiIiKCgUERERERQUCgiIiIiKCgUERER\nERQUioiIiAjw/wH6JplDCzgd8AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12dbf5fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1027,
       "text": [
        "<matplotlib.text.Text at 0x11b5567d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MiL+NiO+Wf0f7zvdcIuL55fdk+nFPRCw3lzg+Bv8fWh0RXyyfYbIyycx59WBw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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b58ed10>"
       ]
      }
     ],
     "prompt_number": 1027
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are several outliers to be accounted for. Lets limit the data to annual income of $200000."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2 = loan_2[loan_2['annual_inc']<200000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1028
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.annual_inc.hist(figsize=(10,5))\n",
      "plt.ylabel('Number of Loans')\n",
      "plt.xlabel('Annual Income')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1029,
       "text": [
        "<matplotlib.text.Text at 0x11b7d6210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NfHCi/cwkj01yDHAssLOq7gG+leTE5qKQs4APrfFZr2F8UYn0CIy6DkC9Muo6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\npAXz/wPSgu4IAH5fqgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12dbf52d0>"
       ]
      }
     ],
     "prompt_number": 1029
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Much better!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's take a quick look at the funded amount. We will plot funded amount both from the unfiltered data frame and the filtered data frame (annual income < $200,000). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print loan.funded_amnt.hist()\n",
      "plt.title(\"Loan with income maximum of $8,000,000.00\")\n",
      "plt.xlabel(\"Funded Amount\")\n",
      "plt.show()\n",
      "\n",
      "print loan_2.funded_amnt.hist()\n",
      "plt.title(\"Loan with income maximum of $200,000.00\")\n",
      "plt.xlabel(\"Funded Amount\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Axes(0.125,0.125;0.775x0.775)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YXW8GundiXQsskLS7pCOAucDKiHgAeETSfJXvrmcCX6psc1aaPg24eTqZzMxs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1170528d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Axes(0.125,0.125;0.775x0.775)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oe/5RmbHmERGn92n+xDTzXwBc0Kf9NuCnTntFxGPAW2bKYWZmzTFvvttqx44d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\nTtMtczrw9+lvTu8GnpY5g81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       "text": [
        "<matplotlib.figure.Figure at 0x1212a77d0>"
       ]
      }
     ],
     "prompt_number": 1030
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There's no significant difference to Funded Amount when we remove the outliers in Annual Income. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Cleaning Loan_Status\n",
      "\n",
      "Our feature \"loan_status\" has seven unique values. To do our logistic regression we require two.\n",
      "\n",
      "Below, you'll notice a bar chart that highlights each of the seven values. We'll be removing \"Current\", as our goal is to focus on who paid or didn't pay their loans. \"Fully Paid\" will remain as is and the rest of the columns will be characterized as \"Unpaid\", after all that's pretty much what they are."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.loan_status.value_counts().plot(kind='bar',alpha=.30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1031,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11b5a8dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gPbb/d63BapJOoSLlp74/pShmdc7QbPug2kL1QZKAYykKcQb+j+1/qTfVyCSt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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b554050>"
       ]
      }
     ],
     "prompt_number": 1031
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#cleaning \"loan_status\"\n",
      "loan_2['loan_status_clean'] = loan_2['loan_status'].map({'Current': 2, 'Fully Paid': 1, 'Charged Off':0, 'Late(31-120 days)':0, 'In Grace Period': 0, 'Late(16-30 days)': 0, 'Default': 0})\n",
      "loan_2 = loan_2[loan_2.loan_status_clean != 2] \n",
      "loan_2[\"loan_status_clean\"] = loan_2[\"loan_status_clean\"].apply(lambda loan_status_clean: 0 if loan_status_clean == 0 else 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1032
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.loan_status_clean.value_counts().plot(kind='bar',alpha=.30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1033,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x118721ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x122dbead0>"
       ]
      }
     ],
     "prompt_number": 1033
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 53487 entries, 3 to 188121\n",
        "Data columns (total 9 columns):\n",
        "funded_amnt          53487 non-null float64\n",
        "emp_length           53487 non-null object\n",
        "annual_inc           53487 non-null float64\n",
        "loan_status          53487 non-null object\n",
        "home_ownership       53487 non-null object\n",
        "addr_state           53487 non-null object\n",
        "tax_liens            53487 non-null float64\n",
        "grade                53487 non-null object\n",
        "loan_status_clean    53487 non-null int64\n",
        "dtypes: float64(3), int64(1), object(5)"
       ]
      }
     ],
     "prompt_number": 1034
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Cleaning Employment Length"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2['emp_length_clean'] = loan_2.emp_length.str.replace('+','')\n",
      "loan_2['emp_length_clean'] = loan_2.emp_length_clean.str.replace('<','')\n",
      "loan_2['emp_length_clean'] = loan_2.emp_length_clean.str.replace('years','')\n",
      "loan_2['emp_length_clean'] = loan_2.emp_length_clean.str.replace('year','')\n",
      "loan_2['emp_length_clean'] = loan_2.emp_length_clean.str.replace('n/a','0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1035
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.emp_length_clean.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1036,
       "text": [
        "array(['10 ', '2 ', '1 ', '9 ', '5 ', ' 1 ', '8 ', '0', '7 ', '4 ', '3 ',\n",
        "       '6 '], dtype=object)"
       ]
      }
     ],
     "prompt_number": 1036
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2['emp_length_clean'] = loan_2.emp_length_clean.map(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1037
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Cleaning Grade_Clean"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, we'll be adding a value to letter grade assigned to individual loans. \"A\", the highest rating, will receive the value 7. \"G\", the lowest rating, will receive the value 1.\n",
      "\n",
      "Using the map function, we assign the values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2['grade_clean'] = loan_2['grade'].map({'A':7,'B':6,'C':5,'D':4,'E':3,'F':2,'G':1})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1038
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Filling in mean values for NaN values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funded_amnt = loan_2.funded_amnt\n",
      "mean_funded_amnt = loan_2[loan_2.funded_amnt.notnull()].funded_amnt.mean()\n",
      "loan_2.funded_amnt.fillna(mean_funded_amnt, inplace=True)\n",
      "\n",
      "annual_inc = loan_2.annual_inc\n",
      "mean_annual_inc = loan_2[loan_2.annual_inc.notnull()].annual_inc.mean()\n",
      "loan_2.annual_inc.fillna(mean_annual_inc, inplace=True)\n",
      "\n",
      "emp_length = loan_2.emp_length_clean\n",
      "mean_emp_length_clean = loan_2[loan_2.emp_length_clean.notnull()].emp_length_clean.mean()\n",
      "loan_2.emp_length_clean.fillna(mean_emp_length_clean, inplace=True)\n",
      "\n",
      "grade = loan_2.grade\n",
      "mean_grade_clean = loan_2[loan_2.grade.notnull()].grade_clean.mean()\n",
      "loan_2.grade_clean.fillna(mean_grade_clean, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1039
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Describing Data using Logistic Regression"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "from sklearn import linear_model, datasets\n",
      "from sklearn.cross_validation import train_test_split"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1040
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###First Logistic Regression (Employment Length & Grade of the Loan)\n",
      "Predicting whether a loan will be paid off using Emplyoment Length and Grade of the Loan."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables = ['emp_length_clean', 'grade_clean']\n",
      "X = loan_2[X_Variables]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1041
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = X.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1042
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = loan_2['loan_status_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1043
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = linear_model.LogisticRegression()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1044
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = clf.fit(X,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1045
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.score(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1046,
       "text": [
        "0.77702993250696428"
       ]
      }
     ],
     "prompt_number": 1046
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(zip(X_Variables,model.coef_.T))"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> emp_length_clean</td>\n",
        "      <td> [0.0160344150048]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      grade_clean</td>\n",
        "      <td>  [0.312299443185]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1047,
       "text": [
        "                  0                  1\n",
        "0  emp_length_clean  [0.0160344150048]\n",
        "1       grade_clean   [0.312299443185]"
       ]
      }
     ],
     "prompt_number": 1047
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Above we have our coefficients:\n",
      "\n",
      "0.0160 for the lenght of employment\n",
      "\n",
      "0.3123 for the grade a loan received. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets take a look at the grade a loan receives. For every additional increase in the grade \"G\" to \"F\" or in our case \"1\" to \"2\" the chance of the loan being paid off increases by .3123"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Makes intuitive sense right? Why else would Lending Tree give a high grade to a loan that they think is faulty and as the grade for a loan increases so does the chance of the loan being paid off in this case by a coefficient of .3123"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Alright. Well what about the years that someone has been employed? That certainly could be used as a predictor. In this case, it's not the best predictor. For every each additional year that someone is employed the chance of that person paying back their loan increases only by 0.0160"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Second Logistic Regression (Funded Amount & Annual Income)\n",
      "Predicting whether a loan will be paid off using Funded Ammount and Annual Income. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables_2 = ['funded_amnt', 'annual_inc']\n",
      "X_2 = loan_2[X_Variables_2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1048
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_2 = X_2.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1049
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_2 = loan_2['loan_status_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1050
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_2 = clf.fit(X_2,y_2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1051
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_2.score(X_2, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1052,
       "text": [
        "0.77809561201787347"
       ]
      }
     ],
     "prompt_number": 1052
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(zip(X_Variables_2,model_2.coef_.T))"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> funded_amnt</td>\n",
        "      <td> [-2.38695184337e-05]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  annual_inc</td>\n",
        "      <td>   [2.2999063003e-05]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1053,
       "text": [
        "             0                     1\n",
        "0  funded_amnt  [-2.38695184337e-05]\n",
        "1   annual_inc    [2.2999063003e-05]"
       ]
      }
     ],
     "prompt_number": 1053
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The reason for such low coefficients, for funded amount and annual income, is that the numbers are in thousands, granted they're in $, and loan status is binary ranging from 0 to 1. \n",
      "\n",
      "Let's look at the amount funded. As the amount funded increases by $10,000 the chance of it getting paid back decreases by -0.238 = (10,000 x -0.0000238).\n",
      "\n",
      "Similar, as annual income increases so does the chance of the loan being paid off. Intuitive, right? This is understandable and supported by the positive coefficient 0.0000202. In other words if my annual income increases by $10,000 so does the chance of me paying back the loan by 0.230 (10,000 x 0.0000230)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Third Logistic Regression (Home Ownership)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Predicting whether a loan will be paid off given the individuals home ownership status. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We currently have a column \"home_ownership\" with five unique values: \"Rent\", \"Mortgage\", \"Own\", \"None\", \"Other\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.home_ownership.unique().tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1054,
       "text": [
        "['RENT', 'MORTGAGE', 'OWN', 'NONE', 'OTHER']"
       ]
      }
     ],
     "prompt_number": 1054
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Before running this unique list, using logistic regression, against loan status, we have to create individual columns for each value, referred to as dummy variables. \n",
      "\n",
      "Each column will have a True or a False value associated with the individual loan that has either \"Rent\", \"Mortgage\", \"Own\", \"None\" or \"Other\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "home_ownership = pd.get_dummies(loan_2.home_ownership)\n",
      "loan_2 = loan_2.join(home_ownership)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1055
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below are our dummy variables."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_2.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>funded_amnt</th>\n",
        "      <th>emp_length</th>\n",
        "      <th>annual_inc</th>\n",
        "      <th>loan_status</th>\n",
        "      <th>home_ownership</th>\n",
        "      <th>addr_state</th>\n",
        "      <th>tax_liens</th>\n",
        "      <th>grade</th>\n",
        "      <th>loan_status_clean</th>\n",
        "      <th>emp_length_clean</th>\n",
        "      <th>grade_clean</th>\n",
        "      <th>MORTGAGE</th>\n",
        "      <th>NONE</th>\n",
        "      <th>OTHER</th>\n",
        "      <th>OWN</th>\n",
        "      <th>RENT</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 15000</td>\n",
        "      <td> 10+ years</td>\n",
        "      <td> 98000</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td>     RENT</td>\n",
        "      <td> NY</td>\n",
        "      <td> 0</td>\n",
        "      <td> C</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  3000</td>\n",
        "      <td> 10+ years</td>\n",
        "      <td> 25000</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td>     RENT</td>\n",
        "      <td> FL</td>\n",
        "      <td> 0</td>\n",
        "      <td> B</td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  4800</td>\n",
        "      <td>   2 years</td>\n",
        "      <td> 39600</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> TX</td>\n",
        "      <td> 0</td>\n",
        "      <td> B</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "      <td> 6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>  6000</td>\n",
        "      <td>    1 year</td>\n",
        "      <td> 70000</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> NC</td>\n",
        "      <td> 0</td>\n",
        "      <td> B</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 10075</td>\n",
        "      <td>   2 years</td>\n",
        "      <td> 55000</td>\n",
        "      <td> Fully Paid</td>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td> DE</td>\n",
        "      <td> 0</td>\n",
        "      <td> E</td>\n",
        "      <td> 1</td>\n",
        "      <td>  2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1056,
       "text": [
        "    funded_amnt emp_length  annual_inc loan_status home_ownership addr_state  \\\n",
        "3         15000  10+ years       98000  Fully Paid           RENT         NY   \n",
        "12         3000  10+ years       25000  Fully Paid           RENT         FL   \n",
        "15         4800    2 years       39600  Fully Paid       MORTGAGE         TX   \n",
        "22         6000     1 year       70000  Fully Paid       MORTGAGE         NC   \n",
        "26        10075    2 years       55000  Fully Paid       MORTGAGE         DE   \n",
        "\n",
        "    tax_liens grade  loan_status_clean  emp_length_clean  grade_clean  \\\n",
        "3           0     C                  1                10            5   \n",
        "12          0     B                  1                10            6   \n",
        "15          0     B                  1                 2            6   \n",
        "22          0     B                  1                 1            6   \n",
        "26          0     E                  1                 2            3   \n",
        "\n",
        "    MORTGAGE  NONE  OTHER  OWN  RENT  \n",
        "3          0     0      0    0     1  \n",
        "12         0     0      0    0     1  \n",
        "15         1     0      0    0     0  \n",
        "22         1     0      0    0     0  \n",
        "26         1     0      0    0     0  "
       ]
      }
     ],
     "prompt_number": 1056
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's run the logistic regression."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables_3 = ['RENT', 'MORTGAGE', 'OWN', 'NONE', 'OTHER']\n",
      "X_3 = loan_2[X_Variables_3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1057
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_3 = X_3.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1058
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_3 = loan_2['loan_status_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1059
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_3 = clf.fit(X_3,y_3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1060
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_3.score(X_3,y_3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1061,
       "text": [
        "0.77809561201787347"
       ]
      }
     ],
     "prompt_number": 1061
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(zip(X_Variables_3, model_3.coef_.T))"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     RENT</td>\n",
        "      <td>  [0.219178922014]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> MORTGAGE</td>\n",
        "      <td>  [0.553450501195]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      OWN</td>\n",
        "      <td>  [0.317744061658]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>     NONE</td>\n",
        "      <td>  [0.434085592491]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    OTHER</td>\n",
        "      <td> [-0.654472520962]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1062,
       "text": [
        "          0                  1\n",
        "0      RENT   [0.219178922014]\n",
        "1  MORTGAGE   [0.553450501195]\n",
        "2       OWN   [0.317744061658]\n",
        "3      NONE   [0.434085592491]\n",
        "4     OTHER  [-0.654472520962]"
       ]
      }
     ],
     "prompt_number": 1062
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "My understanding for someone putting \u201cOTHER\u201d for home ownership on the loan application is that they either did not want to reveal their home ownership situation, are hiding something, or are bad at filling out applications. \u201cNone\u201d could be an honest answer, from someone that may be living with their parents. \n",
      "\n",
      "Regardless, it seems that if someone checks off \u201cOTHER\u201d and gets funded, then there\u2019s a very good chance of that individual defaulting on his or her loan.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Fourth Logistic Regression (Years Employed)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using our column of years employed, we clreate dummies so that we could easily run the logistic regression. We're trying to see which length of employment is best predictive of someone paying back their loan."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "emp_dummies = pd.get_dummies(loan_2.emp_length)\n",
      "loan_2 = loan_2.join(emp_dummies)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1063
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables_4 = ['< 1 year','1 year','2 years','3 years','4 years','5 years','6 years','7 years','8 years','9 years','10+ years']\n",
      "X_4 = loan_2[X_Variables_4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1064
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_4 = loan_2['loan_status_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1065
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_4 = clf.fit(X_4,y_4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1066
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_4.score(X_4, y_4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1067,
       "text": [
        "0.77809561201787347"
       ]
      }
     ],
     "prompt_number": 1067
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(zip(X_Variables_4,model_4.coef_.T))"
     ],
     "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>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  &lt; 1 year</td>\n",
        "      <td> [0.391361177492]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>    1 year</td>\n",
        "      <td> [0.452628193435]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   2 years</td>\n",
        "      <td> [0.508271648817]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>   3 years</td>\n",
        "      <td> [0.475809147392]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   4 years</td>\n",
        "      <td> [0.395646588613]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>   5 years</td>\n",
        "      <td>  [0.44917434254]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>   6 years</td>\n",
        "      <td> [0.405863824014]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>   7 years</td>\n",
        "      <td> [0.420517485866]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>   8 years</td>\n",
        "      <td> [0.418944522836]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>   9 years</td>\n",
        "      <td> [0.414805343335]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 10+ years</td>\n",
        "      <td> [0.496719497253]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1068,
       "text": [
        "            0                 1\n",
        "0    < 1 year  [0.391361177492]\n",
        "1      1 year  [0.452628193435]\n",
        "2     2 years  [0.508271648817]\n",
        "3     3 years  [0.475809147392]\n",
        "4     4 years  [0.395646588613]\n",
        "5     5 years   [0.44917434254]\n",
        "6     6 years  [0.405863824014]\n",
        "7     7 years  [0.420517485866]\n",
        "8     8 years  [0.418944522836]\n",
        "9     9 years  [0.414805343335]\n",
        "10  10+ years  [0.496719497253]"
       ]
      }
     ],
     "prompt_number": 1068
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There doesn't appear to be too much variance between the generated coefficients of the years employed. It looks like, so long as the person is employed they will be paying back their loan. \n",
      "\n",
      "However, it holds true, that if someone is unemployed or has less than a year of employment then they'll have a lower chance of repaying their loan. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####For some graphs and charts refer to my earlier post on this same dataset. Below I'll be going over some mapping using cartodb which is a web mapping tool. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Describing Data using CartoDB"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below we're generating a csv file, with select columns, so that we could pull it into cartodb. Cartodb is a web mapping tool. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loan_cartodb2 = loan_2[['addr_state','funded_amnt','emp_length_clean','annual_inc','grade_clean','loan_status_clean']]\n",
      "#loan_cartodb2.to_csv('/Users/olehdubno/Desktop/loan_cartodb2.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1069
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we use www.cartodb.com. A very intuitive and friendly way of generating maps. \n",
      "\n",
      "Before mapping our data, cartodb uses an intelligent way, in this instance, in converting State acronyms into latitude and longitude.\n",
      "\n",
      "After selecting the features we want to play with, cartodb generates a map and ways to share it. One of the ways is using IFrame. IFrame uses HTML to embed content from one source into another."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Mapping Paid and Unpaid Loans"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML(\"<iframe width='100%' height='520' frameborder='0' src='http://shortyskater456.cartodb.com/viz/40d16f7e-6d3e-11e4-a898-0e9d821ea90d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width='100%' height='520' frameborder='0' src='http://shortyskater456.cartodb.com/viz/40d16f7e-6d3e-11e4-a898-0e9d821ea90d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1070,
       "text": [
        "<IPython.core.display.HTML at 0x122d47810>"
       ]
      }
     ],
     "prompt_number": 1070
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above map is referred to as the choropleth map, \"a thematic map in which areas are shade patterned in proportion to the measurement of the statistical variable being displayed.\" (wikipedia)\n",
      "\n",
      "As the intensity of the color increases (gets closer to 1), on average the majority of the people residing in that state have paid of their loan.\n",
      "\n",
      "The number near the point references the amount of loans given in that state.\n",
      "\n",
      "By the looks of the map I wouldn't give loans out to Oregon, Wisconsin, Nevada, Tennessee, Virginia, Indianapolis, maybe a few others.\n",
      "\n",
      "Of course this an average of individual loans, per state, discounting specific regions of the state, and is not the best estimate for whether a funded individual in that state is likely to repay their loan.\n",
      "\n",
      "However, maybe the other features could help determine which state is less likelier to pay off a loan. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Mapping The Amount Funded"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(\"<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/0dce85a4-6d10-11e4-98f3-0e9d821ea90d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/0dce85a4-6d10-11e4-98f3-0e9d821ea90d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1071,
       "text": [
        "<IPython.core.display.HTML at 0x121284910>"
       ]
      }
     ],
     "prompt_number": 1071
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Reviewing the map we could see that Oregon, Montana and Mississippi, on average, have taken out higher loans (closer to $35,000).\n",
      "\n",
      "According to the \"Paid vs Unpaid\" map, Oregon is not only taking out the highest loans, it's also not paying them back.\n",
      "\n",
      "On average, indivduals receiving a loan in Oregon are much more liklier to default on their loan as they are also liklier to receive bigger loans. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Mapping Annual Income"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(\"<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/c2c9b8a4-6ba6-11e4-aadc-0e4fddd5de28/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/c2c9b8a4-6ba6-11e4-aadc-0e4fddd5de28/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1072,
       "text": [
        "<IPython.core.display.HTML at 0x122d47d90>"
       ]
      }
     ],
     "prompt_number": 1072
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There's several outliers in the data that have been removed, using cartodb, in terms of annual income. \n",
      "\n",
      "Before removing the outliers, the income ranges from $33,504.72 to $7,241,778. Which is an obsene amount. I limit it to $500,000.00\n",
      "\n",
      "Interestingly, Oregon is the state with the highest income, lowest payback rate and on average the state that takes out the highest loans. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Mapping The Grade Given To Individual Loans"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(\"<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/57bfeb6c-6ba8-11e4-a74d-0e4fddd5de28/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width='100%' height='520' frameborder='0' src='http://olehdubno.cartodb.com/viz/57bfeb6c-6ba8-11e4-a74d-0e4fddd5de28/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1073,
       "text": [
        "<IPython.core.display.HTML at 0x122d471d0>"
       ]
      }
     ],
     "prompt_number": 1073
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Keeping on track with Oregon, a state I'm not too familiar with,  it happens to be a state with a fairly good rating for loans according to the data, not really. At least for the loans given out by Lending Club.\n",
      "\n",
      "I could understand why Lending Club, on average, would give a pretty good grade to loans in Oregon. The average population there has some of the highest income. We could see that by looking at the income map presented before."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Decision Tree"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Train Test Split\n",
      "Here, we're using the train test split function from sklearn to split up the features into train and test values. Our test size will be 25% of our actual data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_Variables = ['emp_length_clean', 'grade_clean','emp_length_clean','grade_clean']\n",
      "X = loan_2[X_Variables]\n",
      "\n",
      "X = X.values\n",
      "y = loan_2['loan_status_clean'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1074
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1075
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "clf.fit cretes a classifier object, which is called \"clf', and then this new \"fitted\" object, \"clf\", can do things like score and predict."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = GaussianNB()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1076
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X_train,Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1077,
       "text": [
        "GaussianNB()"
       ]
      }
     ],
     "prompt_number": 1077
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.score(X_train,Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1078,
       "text": [
        "0.74403589679670945"
       ]
      }
     ],
     "prompt_number": 1078
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.score(X_test,Y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1079,
       "text": [
        "0.74012862698175297"
       ]
      }
     ],
     "prompt_number": 1079
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Time for Decision Tree"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import GaussianNB\n",
      "clf = GaussianNB()\n",
      "clf.fit(X_train,Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1080,
       "text": [
        "GaussianNB()"
       ]
      }
     ],
     "prompt_number": 1080
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True):\n",
      "    y_pred=clf.predict(X)   \n",
      "    if show_accuracy:\n",
      "        print \"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y,y_pred)),\"\\n\"\n",
      "\n",
      "    if show_classification_report:\n",
      "        print \"Classification report\"\n",
      "        print metrics.classification_report(y,y_pred),\"\\n\"\n",
      "        \n",
      "    if show_confusion_matrix:\n",
      "        print \"Confusion matrix\"\n",
      "        print metrics.confusion_matrix(y,y_pred),\"\\n\"\n",
      "        \n",
      "measure_performance(X_train,Y_train,clf, show_classification_report=True, show_confusion_matrix=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy:0.744 \n",
        "\n",
        "Classification report\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "          0       0.35      0.19      0.25      8866\n",
        "          1       0.80      0.90      0.85     31249\n",
        "\n",
        "avg / total       0.70      0.74      0.71     40115\n",
        "\n",
        "\n",
        "Confusion matrix\n",
        "[[ 1702  7164]\n",
        " [ 3104 28145]] \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1081
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='/Users/olehdubno/Desktop/confusion_matrix.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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8inEFtzgCT9b+xfFyqjted+qZ73R861g7mC2x9PzyhZ660o2o+T0+0b01Z3pq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\nLikL9qmaWv5vMGaJgiSRuRv6xdswEJ4ZvrTvBLj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       "prompt_number": 1082,
       "text": [
        "<IPython.core.display.Image at 0x11ea9a3d0>"
       ]
      }
     ],
     "prompt_number": 1082
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Confusion Matrix allows for more detailed analysis than mere proportion of correct guesses.\n",
      "\n",
      "For instance 2,528 loans from paid loans were incorrecly predicted as unpaid. \n",
      "\n",
      "Based on the entries in the confusion matrix, the total number of correct predictions made by the model is (1,364 loans + 28,709 loans) and the total number of incorrect predictions is (2,528 loans + 7,514 loans).\n",
      "\n",
      "The confusion matrix provides the information needed to determine how well a classification model performs. The perforamnce metric, accuracy, summarizes this information with a single number .777 \n",
      "\n",
      "Accuracy takes the total number of correct predictions and divides it by the total number of all predictions made.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = [p[1] for p in clf.predict_proba(X_train)]\n",
      "fpr_p, tpr_p, thresholds_p = metrics.roc_curve(Y_train,predictions)\n",
      "\n",
      "fig = plt.figure()\n",
      "fig.set_figwidth(10)\n",
      "fig.suptitle('AUC for Decision Tree Classifier Predicting Loans Paid')\n",
      "\n",
      "ax1 = plt.subplot(1, 2, 1)\n",
      "ax1.set_xlabel('false positive rate')\n",
      "ax1.set_ylabel('true positive rate')\n",
      "ax1.plot(fpr_p, tpr_p)\n",
      "\n",
      "fpr, tpr, thresholds = metrics.roc_curve(Y_train,clf.predict(X_train))\n",
      "ax2 = plt.subplot(1, 2, 2)\n",
      "ax2.set_xlabel('false positive rate')\n",
      "ax2.set_ylabel('true positive rate')\n",
      "ax2.plot(fpr, tpr)\n",
      "\n",
      "\n",
      "print \"False-positive rate:\", fpr\n",
      "print \"True-positive rate: \", tpr\n",
      "print \"Thresholds:         \", thresholds\n",
      "\n",
      "print fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False-positive rate: [ 0.          0.80803068  1.        ]\n",
        "True-positive rate:  [ 0.          0.90066882  1.        ]\n",
        "Thresholds:          [2 1 0]\n",
        "Figure(800x320)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZxCQp8+67cOSR0KEDXHaZBoSVVFEOk9S67rowFllpKfz5z0lHI0nLpii7ppLH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\nUNbxmNn+wOnAPvGFs1yy2Ze7gf+4u5u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       "text": [
        "<matplotlib.figure.Figure at 0x121228a90>"
       ]
      }
     ],
     "prompt_number": 1083
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#OLS on funded amount and annual income"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.formula.api as smf\n",
      "# OLS, or ordinary least squares, takes a y (dependent variable) and X (independent variables) (formula = y ~ X)\n",
      "# Below, we copy the data frame and remove the na variables, and create a single variable linear model\n",
      "# to return a test statistic and p-value, to see how strong of a relationship bodyweight and brainweight have.\n",
      "\n",
      "loan_limit_by_inc['log_annual_inc'] = np.log(loan_limit_by_inc['annual_inc'])\n",
      "loan_limit_by_inc['log_funded_amnt'] = np.log(loan_limit_by_inc['funded_amnt'])\n",
      "\n",
      "fig, axes = plt.subplots(nrows=1,ncols=2)\n",
      "\n",
      "axes[0].plot(loan_limit_by_inc.annual_inc, loan_limit_by_inc.funded_amnt, 'go')\n",
      "\n",
      "model = smf.ols(formula='funded_amnt ~ annual_inc', data=loan_limit_by_inc)\n",
      "results = model.fit()\n",
      "print 'NORMAL FIT SUMMARY'\n",
      "print(results.summary())\n",
      "print\n",
      "\n",
      "axes[1].plot(loan_limit_by_inc.log_annual_inc, loan_limit_by_inc.log_funded_amnt, 'mo')\n",
      "\n",
      "log_model = smf.ols(formula='log_funded_amnt ~ log_annual_inc', data=loan_limit_by_inc)\n",
      "log_results = log_model.fit()\n",
      "print 'LOG-LOG FIT SUMMARY'\n",
      "print(log_results.summary())\n",
      "\n",
      "print fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NORMAL FIT SUMMARY\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:            funded_amnt   R-squared:                       0.195\n",
        "Model:                            OLS   Adj. R-squared:                  0.195\n",
        "Method:                 Least Squares   F-statistic:                     4040.\n",
        "Date:                Wed, 19 Nov 2014   Prob (F-statistic):               0.00\n",
        "Time:                        18:25:21   Log-Likelihood:            -1.6346e+05\n",
        "No. Observations:               16683   AIC:                         3.269e+05\n",
        "Df Residuals:                   16681   BIC:                         3.269e+05\n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept   -200.1427    154.417     -1.296      0.195      -502.817   102.531\n",
        "annual_inc     0.2582      0.004     63.561      0.000         0.250     0.266\n",
        "==============================================================================\n",
        "Omnibus:                      270.723   Durbin-Watson:                   1.973\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              191.620\n",
        "Skew:                           0.155   Prob(JB):                     2.46e-42\n",
        "Kurtosis:                       2.577   Cond. No.                     1.74e+05\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 1.74e+05. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\n",
        "LOG-LOG FIT SUMMARY\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:        log_funded_amnt   R-squared:                       0.181\n",
        "Model:                            OLS   Adj. R-squared:                  0.181\n",
        "Method:                 Least Squares   F-statistic:                     3677.\n",
        "Date:                Wed, 19 Nov 2014   Prob (F-statistic):               0.00\n",
        "Time:                        18:25:21   Log-Likelihood:                -13854.\n",
        "No. Observations:               16683   AIC:                         2.771e+04\n",
        "Df Residuals:                   16681   BIC:                         2.773e+04\n",
        "Df Model:                           1                                         \n",
        "==================================================================================\n",
        "                     coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "----------------------------------------------------------------------------------\n",
        "Intercept         -1.3227      0.170     -7.778      0.000        -1.656    -0.989\n",
        "log_annual_inc     0.9827      0.016     60.635      0.000         0.951     1.014\n",
        "==============================================================================\n",
        "Omnibus:                     2667.027   Durbin-Watson:                   1.986\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4344.710\n",
        "Skew:                          -1.088   Prob(JB):                         0.00\n",
        "Kurtosis:                       4.231   Cond. No.                         419.\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Figure(480x320)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ll5705Ame8IwpoMBZWrIN2iMY4ZskcAc7SCGFVFJZYGV9OVJ7pKV+ZSE0q0mI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UAAAg\nAElEQVSFRODU4RQI/ikpVle34c5BjGUnEobKNF6P5PTa0u6/v2QkvLWFYC3oDuNeI5BkdZ7POfZx\nO5EdgEqC98TS5PaD0q7uZ5ynsMuYX57tnkE5Ej+7koBFXXIW3E26rqvZ24/90vb9MnAV2H+B4Bcy\n8mNgPRMuKXsO4u233uY7eIkLSTV2IL/GE46y62MDDPnaECorK/lJ109MZ9FEEzNCM7i963YzH7KA\nBejoxIiRRBK7DOGR9awnSpRssvmmoeT8BE+QTDI/42dcxEXUUksXXWYjXB55PM/zrGENGho96Uk2\n2QxnONvYRi96UU89TTRRSy1/4k+kkWbSkQNMZSpRokL3EUS3c44RdxLnACma0bmVhTPsUgY0wF3f\nuov6znrLQYDTMZyKxO4VCOjsl92CfRdSjX8I6STyC/U+ljE9nWNSCCNOwk25YUcM2c3kEUw/Yh9X\nUZFsQSqLhuBtmLMjAWvFvw3LUXRh5Uzscznd/RWyjXm4GxjjOCdwh4waTjbQdqSNghorGe023n5w\n5yCGMIQFLHBwGS3vu9zUxz4TOpK9r+81HYSDULALFiQvYG1kLUl6Et81hFjcpINzmctX+aqpU11G\nGX3ow2M8Zr5uoIGnedq8Zh7z6E9/QoQcYxVTTCaZ1FHHCEawne3mOGDRkW9jG3dxF9vZznCGs/3o\n9vOqI6EQ75M4B6jYXMEdE+8g/B2fLG4l5GfnkxZL451hNhm1arz9EduQVVUHYvxSEcN5LV7jX46s\nrP8GfN84dipa7RuMe/wZMfqJSKzf7tSeQ8I5Sjr1JLJ7UJ3Z7jmDOLE6xJFlYeUtgvoT7N8DrEAq\ns27FyknYcyhKk1o5BnuIqNGY4wAsapJq/OVZ7fQlIDmJK2zzKLrwqMI/i/ioDXlgNeXZUUUV5ZQz\nkIHEiNE5tPOU/QJuKBr1oB4GO/PsmfQ5uJlq/ZhrQXYCdsehMIlJzGVu4L2e5El60pMssogSpYEG\nJjKR9aPXn5KqPU4V/hnBmBvHkD8wn3ccWpoGGmFP/R4x/E1YCdw8430jPJXemE6f3D4cPHGQCBFJ\nRmcg9fx/QpyGnfCuADHGdmW20+U28hAncQ1Cv9Fi3D8BMba9gDG26zbiLE9Uc66U5yIbMfjfxEok\n/zeywl+N/HZFjP/1xj3tDgLEKSmDrkJO67CIBwfhDDHFsHpCokhorZ9xjYYV+lqFxVjbjjjENYhz\nzMCiOY/jE8WZqNfZlelaYi1EO6PcjtNJFFDAK7xCE02ECXNy10m+3v3r3PHYHWZjXmVFJb+c9kuO\n7z6O3q7TmthKUkISGQkZtHa0sotdvmSA4CQaTCTRk78YznCOcISJTCSJJJJJZjrT0dFJJ50OOqii\nylPCG3S/NINfpokmVrLSI66UQAJhwtRQQyqphAnzE35C+qvp5303EXcSHxNKba6ptQkX35hAhTRU\nhY8Kx+Qh4j0j5GXHsQ4aOhuIHIvISl4pyyUiTiGEv9a0SuquQ4xzNc4y1u5IXmIdYkBjyGo+DTHQ\nHYhxTcLpIEB2Gat8xsxHDL4KE+3DyklsR1b++UjOIIpFLW7/PVZjhrDKXpUy3SCkk/o+n/N1Yw5J\niOGPGNd2ITuvZOBS25ySjeMxxDl0+jxnHJ8Z+CnTLWCBx+BWUYWO7uh4XtGygvKfiXLX1SOuZsmD\nS0ivSWc606miite6XjOrh6qoYgtb6IE/caGdIPA4xz3hphWsIEaMPvThcR6njDL2stcRAnNLlIIQ\n8vmhk06qqEJDc+wk1BhhwmSSyRzmmO/NYx4HOw4y7755sOrsJGnPBPFw08dAUXERP//dz+m6pSs4\nFGNfOauwzxpkR6AYYFV4RKm6Xe0z1qk4irojJaDucE01XoqPcsQBfQ/nnLfiz+VUiuww3CGgFLyC\nPyo01BtxFjrCLFuHlaC+zXXfZxGn6B7/EELL4T5fQTldpY431nX9EcTZfMt1TXe8VCEXqDLd5xE3\n9bzJ0Xyn8FN+yhCGmCvsD/iAucz1nFdKKcdyj3H5sMtp3NRoGlx3GEe99uQkEON7lKP0pz+P8qhD\n3MiOJ3mSp3gKCA4v2UNS85nPcY5zCZd4eibe530yyfQdQ4kS2R2EwlSmcgmXkD062zfsFA83fcpY\num4pXd82GoHyjIOViFHsiTe0on5U2cbXfcgq/xKcIZY38BZL+lcJiiG+GjGAzUhSuBorieymzBiL\nVQ5qN7xBCV/VT2zHrfg7rVuRiqWTrvdU010b4hRiOHsW/MZfhThStUNy76JUWa1feau6/lsB1yhK\nFMUhdZbVTXGceyR1+VdppJDi0Zr2C+ckkECoK4TWoTm6mN0dzeq1ul5pYyviP0UoOI1ppAaUX2XY\nGqP8dDAAqqmmiCI66WQkI8kll6EM9WhxK+1rP+SSa4oPuZFKqvSd+G9QzgniTuJjoCvR1SmaZ/xf\nizO0onACMZjtQDIkNSYR0SPeBrIXcFbxgBg2u7woyE4lgv8quxZxGn5QzsqewwiqsAr6DVG5EHsI\nKg9Z1bvDOaOQHU8PJG9h3+EHVW4lIc8/ktOXtfqtk4LKA5uRzysf+YzOf5l5HB8BkZC7XE0wgAGO\n10/wBKWUepxEjBiNzY28/dbbRIma8X03Pbe9w9lO/zGd6bzIi5RTThppfJfvBgoSqbzDDnaYPRZu\nhAiho5NPPoUUspKVvnQjv+N39KKX7xj11NMc8MfcTrv0nZyFJO2ZIk7w9xFQsbmC/OvzSfpKEqEv\nh2iuD7DCfsI2ZUiIZhTSRT0K9GRddgLulfRt4PqdFgPcgISqlO5CJsHiOvshINxqlYPamznzsFbY\n67F0FoIkjJXAkV3zoRoC2AUk/3Ez4gybsXQ2gmg97LbidGWtftGaoHLaHsZ83wdDPjiOzxBueegW\nfhH6heNYMcUegSGAoxx1vF7Oco5znLu4i8KThXSnOw/wAPdzP4UUMp/55rl++hIllPAP/ANTmMJ0\nppNNNi/zMjFivkR+fejDZjbzAA9wJ3d6zimhhHbaKaCAgxxkBSt8NTSWsQwdnXrqfe9TSCH96c90\npvuOfzzr+HnVlIjnJM4AFZsrGD9rPHuP7xVDr7h/3kASwPafjypN7Y0kd+sRw9mFt5sYRLf5Ltvr\nasTg1iPxfLVCfw6J3dtX6VsQ/iE/3YStwGC8uwxFUVGA0Fe46T7cdNpRZFXvLin9Kt7qoOeQz8f+\neajnacCiIK9FwmxjCS57PYZVGqvG8JunX06iHMlJDMYZcnLniJ5BdD1eAV6L5yQ+K1hYtJCXlr5E\nqCtEY0sj2dFsj/40SCcyCIdSF11kk81N3EQBBb6lpFVU8RzPidYEERJJJIccwoTppJNCCj0r/FJK\nOcQhxjKWnewkgQTe53160YsWWhw5hCqq2MlODnCABBK4mqt5lVdJJZUIEXR00kgjQoQkkuhHP45w\nhHbazeT8JjbRSCOX4dTSAMnLxIiZGhpRoiSkJTB73ezApPWnnpPQNK0aCaJEgYiu6yM1TctBTN8g\n5M/7Tl3Xm4zzpwDjjPPH67q+yTg+DPmTTQVe1HXdybz1KcJkd9VqnHHxjUj/whvAauiR04NQLMSJ\nhhNEukfEyCs210Ss7l5l9BXsK+9qvMawDDH4CcCdrsmNQmLvftBx5kkUZYVRTcV2ZFVfbXu/DaHu\nOGm8X484kSRkB5NiPI+Gf/lo1LiHCov5Pc8GZAejci72sleFQUi5cCsWqWErVlmt+sxqjbm0IPQh\nycgOpBnJCV2Bs6vbnSNKM94P2i19weHHtPpJ0EA8UvSIWcY64boJDPnDEA9B3gIWMIpRFFDAMzwD\nYNJhgD+ragEFZse0/Xx1vdtBgOQ4csgxQ0TLWU4vevEYj5nX2cdX81Fj72IXaaTRl76O+Sk8wzPU\nUBN4vR0ZZHALtzjmuX7k+vP+M/m4OQkduM5FWjYZ2KzrerGmaZOM15MNLv27gC8jdSuvaJp2qbGM\n+jfgAV3Xtxm8+9/SdX0jnwEsXrPYy+4KVsK0D/Ro70HTG00AXHXLVbzb8q6EVqrxzxeAZawSsEj4\n/HQTChFH0D9ggjG8uQRF1Kfuo+5l52Xa5/M+eKuc/MgF7QbdPZc0ZJeiEsPuBPytSDjLDnvu4Xpk\nZa/7XOs3v2qEh+rbtmMbkZ1KHpZ6nd/fUQxrZ+Yv/fGFRRDT6v9v78zj46iufP+9klqrbXmXDLaR\nsWFgAs9hjZPAxIQdwpKZkBAnTBI+THhvAvhNhoQtjyGEfFgMBGx4b/LB2SYGAk7iLQYDxiYkExYb\nDAYnELw02MTygixZtlpqqfu+P869Xbeqq1qt1UbUzx9/VF116557q6vPufesMDCullECyaYMB3zG\n3gyZ3PksWUlT4aC7rKpu+6i2tv32su38vOvnOSPzWtYWRQOgldbcLuIn/CQX9+C2dW0b1n4SZpSv\noy5XjzssH9VAoU/qJqXUFuBErfUHzrm3gM9orXcopeqB57TWR5ldRFZrfadptwLJ4v8usEprfbQ5\nfykieP5ngNagbsuXP7OcuY/M5aW/vERLZYvHmFyf/hSi6qiGuuo69qb2ksqm5HwZIgAKldBcjOzD\n0shuoxyp5uYaggH+C1kxT4y4ljX3Jsx42pGV88V4gmoP8kKNR3YKu83nUrwI66lIMFyV6SODF6Bm\na1B0IivvUeSreDD91CA7kgReGdTxiHorbf6HPZcFpv8K8+zKzfhGOONbgz83VphgBfiZub8M2SFN\nJl9dtQ2YaZ7lLbG6yUVYlDPQbYRvbxAqkKY+zJfvl6178JrN/2RX9idzMotKFjEuO84XB7FSreTf\n9b/77tvKVv6JfwK8VBuv8zpP8RQ11HAKp/iYtRqpuHD2hWxYsCE3BhtjcTqn57nPumO7mZuppTYv\nxYatLHcf93Eap/ECL9BKq0+lNoc5OdVZsF/rVrtg6gJm3T+roNA+4OVLlVKbkc19Bvix1vohpdQe\nrfUoc10BTVrrUUqpecCLWuuHzbX5SMmZJHCH1vpMc/5U4LvBko+D+WNa/sxyLxmfXfm7qayDqbct\nsz2d4mIPfo0w2hkIg0qSH89gVVMgzPWiwLXhiM0jgzDUT5prlnbS3Af5zHx6oK17zU2i9zjeXtPW\nx25w6O9DhE87stu4CBFaIwNzWYqojwqVI12ECK9Dyd95WbpLEbfYk51ru/B2HLbfNPJW2uf7MmK7\nqMIL2EuZvtoRtdq7sZBwYdNWBLHoM4u4/7n+FRLdCSQ3lUdTaxOdupOKzgoatzcyqn4UdRPrmDJj\nCi8ufZE9yT2UUcboKaM54YITeGXZKzRtMTaLhlHUHVXH+t+tR7UpmnUzVRVVTJs6ja27t7KzcScT\nmeirRT2/fj6Xz5c4hyXzltD8fjON2xtpL2mna08XXboLlVHUVNTQ2tVKZ6aTWmpJkSKjMtyt786b\n143ciEKRIUN1aTVtmbbQGIjruZ6jOCrPNnFr6a0ce8axRaU0OeA2CeDTWuvtSqlxwDNmF5GDyaN/\n8P0CusHcR+Z6yfimIszWJqMLxgZciOjNrd9/MbEHafxxAm8SXgJ0FcIEvxS4ZsfkqmNsnQYrEBoI\nX2XbOAld4BoIwx1DuKrMjs2+nwscuqUhc7HPyPbVYP6uQp6pRlb9QQHh0mow/awKXLM2mSTRqr3t\nyE7GNWKvQNRhXzV9Br3JPuKISvM9EOqNqNTf1ve/mFQeQHh1wbBzEQgTVlc0XsGieSKseqpmmz1z\nNvw+//yRHJmzOSw6ZREbX9sY6q5eWVLJ17Nfzzt/6PRD+303Vwh9coHVWm83f3cha8GTAatmQik1\nAfkpgtQQm+TcPhHZ8L9vjt3zYfXGuOWWW3L/+7tEn4sO7YXk04CsvK0ROgyur39Y7IGLZfgNpUmi\n3TV3ER4DsAn/7gCEObYZesmQsbhQ3VyzNMIY9uZAO5D5W7pR8QnBeIgGRMjYDK8jihhT8BhEwDzb\nzXjT5AfW/R2yC1pNLCBCcPE1F/Pw1Id95xZMXTAgrpaDKZAKoTth1VMUVU+jMjo2hBrZybh4qP4h\nvnHrN3o3oF6i1zsJpVQ1UKq1bjXFV84Cvo8oBb4G3Gn+2kiUpcAjSql7kTXjEcDLZrexVyn1CUQx\ncBkwN4zmYJV5zKX+tmgw/8NK3UO0T38DIiJtjQmNGJT/22mzieh4hnFgHB/8iGKm9Yh6ywqmQvEF\nUfs7e747hu3ePw4vP1VH3h2CiN9BzvYXNdYgreC4FV6eqDAoCC0fPAVxsT2NeCcRgmLSbQdRjDdU\nWJtCpUrD7tnZvpOtf9pKha5gT3YPFYkKxlSNobOsk/OuOi/nGRWkufP9nSTfSVKeLqeTTtpoo4MO\nxqgxlOpS0iodWqfixd+/yEnqJErMvwwZOuigkkoSKkHpsFImTJ7Axm0bKW0ppYoqUqRoLm3mHd7h\nOqf8uU3BcR3XSbunUuxiF7dzOzdwQ67dHdzB31r/RlNrE9dzPQkSdNBBujHNNRdfw0hGUlNRw7gj\nx3H5Dy4fUA+nXtsklFJT8PxUyoCHtda3GxfYxxFTYRK/C+yNiAtsFzBba/2UOW9dYKsQF9hrQugN\nqk3iH2/6R9LnO0v8FchKvYz8XEgZvCCtJP5VbVj67qUIsxuGGK6PI9xV9FjCYxmi8ji5KqBVSG6o\nYNpsW76UAjQbIsZt+80Snfr7Z4iaKviMdprxhMVsWHfZ7uI2wsqrtpl7agqMtw2/95PFb5EdzDTg\n57FNoi8oZHy2DKw7A3VYCvHgPYtZzEY2ci3XhuZd+lHZjzjtptN8mWAfnf0ox2w6hud4jtl43vV3\ncRcg0dsgBulneIZruTbXZg5zOIIj2M3uSBdc224f+3y1re/mbjrppI46SijhHd6hkUaO4qg8I/Vm\nNjOe8UxhClmyjGZ0aMLAkziJp3maIzgiV8vC2k1Cq/gdaMP1YGKwhcRXv/dVmiubZSWqgVYoT5eT\nPiydn7H0XUTNsh/Pm8ZmKc2Qb1NYglfLwTLjJKIacejlmOeLiE69A1F77SOfEQeZ9SJEcO1E1C1V\n5vxhiBorbcZrV9ldyO5hAl5MRRn4FlZLEAP0SERQ1CKMvwFPODYCnw7M5XBk96QNjRJkZ1FmxjES\nv6Cw4+pEDOQjzDPdjkRuV5nn/TFDs8X8HwXmdyP4lelDm37cuSw218oN7VtiIdEXFOMN1RuPqeA9\nxdR5uH3M7Ty1+ynf/WFtowLulrCEqlFV7N6zm1nMYi1ru60nEfYZ8utH2LoRQQTbdVfD4kZuzOWY\nguhneDAYrock5j4yl+bzmvPOJ5YlSI9PCwO3OYvGI0ztNCRnUxqJLN6HMKEwV+pSPB25mzOpwZxb\nieeB1AA8jzDnDJ7aph5ZJdvI7GCgWApZlU01Y2lGGO37eMLNegk9av5WIZHKVuApJICtFGGydkw2\nktymCv8LngG6gvzYC8C4d0u/NtV3KfIG7kcM0DVmzAm89OIt5pwVKsMQYVmJl4Nppfls+ykz94zE\nM+6/bK7ZvsciCtIk+QkCi4BS6u8QMWRxOPB/tNZzA+3mIlEzbcDXtdZDMiKjGH1+b3T+wXuCdR7C\nUNblsTV7f1jbQgF3tf+jlv2/358XgOeiJKCTDX4G8pIDVuVWa4XbRc3N0jiSI/0xE3GCv8GFz3Dt\nILUnFe5Bsw9hNtWEu6ouxb/q34+/RsM+JPK3HWGgJ+EXGCUI83VTclh30F34o6vttSziuWPLlw7D\nnxbElgLdjjDnFkPnq4R7Ci0zfxvM/4cId9vdRzhKEZeELcgOJJjCex+yi7FpvMPGYNVTrqptuZlb\n0NMrhadiSpp+w+Zv634/HzHuCGit30YUhSilShDx6wsTVEqdB0zTWh9hbG7/D3HMHXIoxvjcGwN1\n8B63zkNUMFtXmecZYu8Pa1swGK7Sq/1QTNBc2GfIrx+RikhWFmzXHc0sWa7gCi/JYZzgb3CRZ7g2\nyCay4R40ZYhKKMzjaB9iA3gYLzFfGx4DPM3cV2n6OQxZdS9EVr4jkBV9MLPqhUjRoi/hJeZbjefe\nWmPaHYsIjKBb6gXAO05fh+CpasI8hS4w47I0KkP6tM/C9ehK4hU8etuMKyyF93BEbWfpho3hHNPO\nxfnIbi7Yn2soj5qPNVavpK84A9iktd4aOH8h8AsArfVLwEilVF2fqR2EKMYbqjceU8F7pjOdu5HY\ng7BkefeW3cu5V52bd/+JnMj9+NUxu9mds0tY3Md97K3fy0VXX0TDPzRwN3eH0rmHe3xJB+/iLl+m\n2dd5ne/xPSqo4Pt8n3u4h//gP/iAD5jDHF9fd3EXrbTyIA/mzp3Iibl5WjzEQ5zACbm/IDuLh+of\nGtAEf/FOIgTXzLqGTQ9u8mIlQBjJyIgbWon2ULJ5jtbh6cQXEC5sFpJfi2EF0Z4/7s5V49kAFP4q\ndeEyT1QvaYTxu37aUUuHsXhJA6P6rDZtViFr65H45/ObiPsU/rcxagwt5OfACtNiuJvBqL6yeNlu\n/xjRpjhcSrjv26GAKzi2IfupHX2idhCiGG+o3nhMhd0zLDWMm/50ExW6guZsMzckbmB01Wi6yro4\n96pzfd5N9v4l85ZQsa2CG965gUQ6QRddtNFGO+3cpG6iVJeSqcww6ehJfPMH38zFZlz6mUt59PlH\nyZDhOq7LpQVpp51HeISFaiFlw8qon1zPrm27uL7lerJkGcOYXEEiEKPzGZzBr/k1jTRyK7dSTjlp\n0rTQQhttbGADN3ETR3AEWbJMYxo3cRMAXXQxnvG8wiu5yGuAv5b+ldvm3xaXLx1snH+mLNsvu/Ey\n9tTsEcY7Da/0aBAZouMD7G7ZjY2Iagvhq+eoJH6uIAgmBcw45wu57lpHE3f1HxRKlkYT/iSBYbCq\nrwYz7mAa9ChBqxEVkRUAUYKxFs/dFkMnqMVI4iVUPL1AX2lkV/daxPUioJSyZZmui2oS+Pzht1JH\noJigt6ID4/p4T3/d/83vfpPFlT1LchgalGdUQw00hBqkb6u6jQmpCXnXLuZiFp29iCkzprD6h6u5\nvMszjN9bdi9fvOmLB32CvyGL8888n5MeOYmnG572Tu7ES8ZnsQxhbq4B2sJ6HC1D1EggDDwqliAK\nCfILDtk+XyLfe+piRFdvPXjHkm8XWQK+GifD8ebmziVJuB1mONHztf2HvV1Tgd/hd0ldhgi1T+FP\n+1GofxuJvZ58o52t7JdEnkMToupzvZtWInaSY5CUHb3HucArJqA0iLAA0rxAUTf+Z+bMmcycObNP\nA4rRP+htksMoA32YYdtiYmpi5DXaye2Obn/gdsq6ykJ3TQDPPfdcvwcaf+RdYG0ivw7dQYWq4JpZ\n1+R2EsufWc5lt13Gns/ukcZ2lRt07/wjwsgrkZVpKSIIlNOuBs9l9HnE1uC6a1oPo2CZTnutE/Fi\nSiM7iAxeveawbKmLEOFl0388jewCbBLAFPAvTvvg3PYiTL6Z6CSFh5v2u/GM7tVmrEeQX0vaYokZ\nu302O5DcUw1Om4V4CfrS+N1t3TnuB07B/510kJ+FdyHy/NzvrQHZZU0DnuidC6xS6lfAk1rrX4Rc\nOw+4Smt9nlJqBnCf1npGoM2QcIEdiuhtksOo+37KT9HoSNfWqGt9SaoYx0n0Eb5EfgBJqFpXxdTJ\nU9myeQv7W/YLk7LZSEuBLwQ6SSI6fddHP4msZkEYXBYRIHZlb5l7CWJXsFlWT8ILKLMuth+Y+xJ4\nO4YknmfU+4QXM1qFGM2DQWYrES15AtGY22yqWxDmbgVJJ7IDaSa/jgXIzqTGjGE7/l2ApZdEVumF\nCv+Al47cLYW6BS85YqHAvj1gknp6NG2mWKsaayA62eJq5Lt4tedCwmQaeBeYorVuNeeuBNBa/9h8\nfgB5AvuBb2itXw30MSBC4kDVgugOs2fNZu3CtVToCjpUB5M+NYnxleN941y/Zj1PPPAEpGB/ej+j\nDh1Fzega2lva6WrqItWVooMOJh02ibLyMjrppH5EPY17G0mQYMyIMb7jD/Z+kGvjPouzjzmb5g3N\nuSJA5ZTTQQf71D7qyuvIdGS4lVsBfKm83+It9rOfaqrZX76f2mwt7V3tKBQVVNBFFylSVFFFDTUk\nSLCb3dRQQxttVFLJbdyW63Mb22illf3sZzSjmcCEXM3rJprYwx7GMIZOOumgA4WiqqaKiUdO5Mof\nXDngWWA/0uomXyK/JLAJUp9P8SZvwgn46zKsIT/HkrnHl4bangu6ZFpD6yJkJTsKv9rqd4g6y5Y4\ntbDpxIM0bZunCVclVSFM3Ho+7cPb3QSDzp5FWN1Y/IkHl0Jo6d6k6ScsmV5wR92Bv6jRfvwC4nFk\nVxXmVmwxlfD63tOQ1ObuuDbhN5QXk6LkQuDViOsFoLW25Yzccz8OfL6q5z33DYNdC6JYzJ41m02P\nbuKH/DB37u7n7+aTfDJniH1wzYPsbtnNf2S9im93vXsX6XfT/Bv/ljs3n/k0bWjibM5mDWtQKBIk\ncqm/P+ADX4T0fOZzOIcznek8vOlhbv7Xmxn53khmMSsvavtufTfDOoaRMAnHwiK77+VeqqlmX3of\nZ3Imf+SPvjTlz/JsaLT0dKZzB3dwMzczkpG5NlH3nMEZrGFN7t75zKeJJtgPretamXfFPJg/sN/r\nR9oF1hcPEeUmuQGP8XwKYVbPIivQFxBm5jLx7hLjfR5R9QQNup9DGHXQPfRiZBdiiwsF+29BXGxd\nF9jpiMDZhqzkD0cExiXkCwg7vnLCs7eW49WKsHghZPx2jlm8+tWvIjuEw5FV/Pl4Bm3rDkzInK0r\nrcVGROgtdOY4zZwvxRMEhZ59WLLFlWZsQwyL5y72CQiAr2z6iqS9OIBYu3CtL+UFwLVcyyu8kvv8\nrT3fYlJ2kq/NGMb40mmAGILHMpZXeCUnGCwTX8taH0O37S2dr2z6Cp3vdXIt14a2vZZreY/3cq6v\nYW2+zbd5j/dyfVgBYem7zD5I/3qup5RSX5tC97j32nnbf8Mbhw/49/qR3kn44rjOl8kAABVOSURB\nVCGixGUH/hiF4Eo/uOotJpNpMCNqd7DtU+Sv7MsIj3B+GRlrB7LatjuEqPFFeVxVIkFuC/H0/eX4\nVV5WRdSCCMz95AfZWQSD2n4bQbcVsRfU46mnFiNpP+oR4b0HL8HfKvzC2oV1QwaJVzkEz2OtIfyW\nDzP6O5tpf6FCh/tNdxe53F30MRQXie22t5HPUW0rqcztbn4T4bdto6SDffR0vMXc496bZwAf4O/1\nI72TuGbWNUxdN1U+RKkjMngr4zWEr3rbkHgGEAYZBlftHJURNer8OMQe8TlkZZ50rkXVZ+5EdhQV\nCJO3KOQOGnX+XUS3/0WEwdqUGDYY8HTzuZXwHYldzYet9KPiS8Yju4TtSIzJQkPbZm49H4/Zb0Lc\nYkdE9PU+sgPZjDzLfaZ9g7l+YBfY/Y6DJfV2EB0q3K2vu8jlQtHHtm0xkdhuvzbyOaqtjYCeznTS\nET+OqIjsYuh3BFwci4mwds+5/wb6e/1IC4nzzzyf+791P8e/ejwljSX5zMK6ZlrmNjavC0E9ssJe\njhhLg9W5XbXGIkR1sjykTZb8MQRVIhcg6h6LwxDbQXDc9r1uNmOyiFK77I+g/WlETbUJL/6glHC1\nzhiid0lRNSysS6xFEhEI+0z7TyMquksQL6hhpt0y5LkcieSbWoio2IJzWIyXAPCzSL6mOmRH8Riy\nWwlPp/OhxWDWgugJTrwkP4p4DnN8kcsPjHqArSX+wPXd7M6Lln6Ih9jFrlwEstXXQ3gkthulvGDq\nAhKTE5HR1HOYw2Qm5+6rpTYvSvoe7mEyk5nDHE7kxLxo6Xu5N5L+ndzpG29397j32nnbfzY6fCDx\nkfZugoCHUxJZbe5CVpzDEOZo3VILedl8FmFUKWR1bF082xC1SBkicFJ4taJH4M8mezKSxCGBMEhN\nvmsohk41Ipj2I0yvJdDX2+b4G+Qbu5OIOmo4stM4HPiD6Wc3IgTGke9yalN+lBDtKZSNeEY2oG9W\nyLWfI4y6Cnlmrr0jGF1teZ+tIZ5G7C2uZ9kLpp+s06Yc2Z1YF1hb47sGEUjrhlYWWLfkp5t6+0Aj\nzLuprqrON871a9bz5ANPolM65900bPQw2ve209nUSaozRZo0Ew+bSKIiQafupG5EHTv27iChEowe\nPtp3bEue1o2o8z2LMO+mNGlaVSt15XW0d7aTzWZRKHawg/GMp5JK2mmnjbY876YSSiinnAyZnBeT\nrQORIcNwhpMiRQstjGIUWbKUUEIppXTRRTvtVFNNCSV00UWGDApFJ52MYESujUJRXVPNxCMn5qLD\noxC7wHaD7mIg5j4ylzVvrmHP5/b4b0zi1WFwXSeT5KtMXJfO1QjzCav1YGtA/BrRnY/HH1BmmaF1\n/XwWUbVEubeCMPHnkHCt4JgaEQZpmXISL3sr5CcR3IUnDAu5i05B1G5R9SwOR8qxBoPlrJdYWHT4\nxxHX33SBfu3v4DFEiFpBvJFwwbMcEQKn071wfxb4w9ASEjFiQOwCWxB5MRDApge94ytuu4JG3Rhu\nB2hA6h/YVNzuecz5FoRRucbP3UTnNLJqkk7TJlgEx0YQu59/gahijsEzEu9GdgrbEO+hSjzDrV0l\nT0N2L2lEODQ4/5eaftYhzL4czxsqiZd+Iww25caLCHN3vaRcYfl6YDwd+HckjyB2Eo3stGyB22GE\nw1Xf2ip4mDGE2/v8DgclyM4pWAfE/nROR3ZSMWLEyMOQFRK+GAiDTcdtYt6j89i5Y6cICLvKDIPN\nMVSBnyE2IG6lJXjMCoRJnoRXNyEI248iWm/fBJzofK5CbArB6nLPIszxIkSINJCvktpsxuem+E6S\nnxF2GcKkOxw6SfJX34sRVdlCZLewHlH9JMycrHfUCkTouONZiF9YvY6nllqIMO9L8FdncGG9x9y0\nHCDfyWMR97iCZQeeC7DFMqKdDGLEiJHDkBUSUTUh2rPtJHcmvZV8MOdSEmH0FcguogJhgraYzXjg\nKNPWrV1tmZeNKwjmWWpB0ke8gTD+MJTgZ65lCLMLi0mwhu9jzLHrpusy0wvMOLcQrgq7AEn7UeLQ\nsWNYhQiQoG1kmZnnyADdRYiHUzDC+iREaDWYeycgz9FWjrNpa2wsSFB1ZoWTqyKzyETcE3QLDD7D\nC4gWSjFixMhhyAqJqJoQlSWV6FJHB9xg/q5CVDgjyI/YnYro2a3rpIWbOiKJqGpsGc5ViDBIIR46\nW/BW0r8iX5AsAY53Pi9CBEt1xATteTv+R5FvM6xK3VgzztURfdWTn7zajvVxxKC+ycwhi9gX1pBf\n4+LzyO5iCaLqKsPbNbUgz8Teu8/MIYXnlpsgXHW2E9lVuXOim3t24T3jKBWgPR/0RosRI0YOQ1ZI\nhNWEmPrqVK6+6mp2ztvpL0nYgBcJHBaNvArZefwX/iCyHYiuuxXPRpHEr/5ZYI5txDWIF1ENft38\nBNNmC5JbSSP69yj3TNfWafvX+IVYsG2h1BQ2HiQYIOcWSLJ4luj4jFKE+boCcAV+9ZwVrjYAzu7m\nykNoLTO0WhDhOs4Zn43LaCBfgPw3ovZaQrRaqQ3ZoRwWcT1GjBhDV0hYL6Z5j86jPdtOZUklV191\nde56YnGCzuGdOffK+j/WkxmXYRchGZ+tgTOL6NNdBrgUSYvRYD67dQ5ADMvWg8dC49WX3h7o73HE\na8fmM0oSbh9QeALJ9R4qlF67iWiD8+aQuS0h3KX1dDxX1CCy5AccnoM/slobuk0IA38JEZopvHiN\ncmS34aqYluJ3h12B1BMPznkFno3EBhMGU7zbVOln4Y/TiBEjhg9D2gU2iDCPp8oVlRxScgi1Y2rZ\nsnMLzeXNfkYEnqvkAqJdUt0VvHW/tN5I4KmwXC+gsNTjbciuJeh6a9u4mU1t4FgVXmW5FoQxVuCl\nFa9EdP+dCBOvxbOlHI64kf4N+OeQuT1MeKpwmwU2KJB2IjmugjsSW5nPBrdZY3cKsU+4qitrwynm\nWT9m5jUKUV9pM4bReEF/USneNzt93RK7wMYYeohdYAsgLEYizOOp/Zx2ti/ezuazHH3QswijsWqk\nckTVEfW0gl9BMN/TEkRVVYIYe7cgTLQRYWINTtsnzV9XNdTgtFnlHF8E/BJhepcgwiSYarwZETyV\nwKmGtk1wXYbYB2qJzt1kDcMuw29AGPA+RF1ja1t8DBE2YeqpZrxCRJOdvv5Avm3jAqKr6QWftZtC\n3cVCPJda6xDQEGjzlwgaMWLEyGFIComoGImqbFWo8TNVm/KfOB0xBLsFgFYQrYcPLgSDJTovQnYh\nY/GrYpbiqYwwx9bds1ClOxeVeOojKyDCEhBeYo6bEAZtdzZJxOPKrVKHc21kSH+vIG/ORYHzkJ9C\nHPP5l8juxp3/CqLfwKh8NMFnHZ7yxqsnAdG2mH0R52PEiJHDkMzdFBUjsb1xe/gNYbv9+sDnc/BW\n1S5sDiGLJYSnnw4ySJB4BTcP0yZElWRjH6zXzm8RoRWWtTQdOA5j0AnnuCvQZhP+kqUu1hCerC+N\nX0DY85uJ9iQqJ9xWEcXAK0LGE3zW1hU3DBqZq7VjBPta4Yx1ZUQfMWLEGJo7iagYifqx9YxcN9In\nQKpWVJE6KpXfOExwjEDSUixE1C3teEbfLeaeND1PP23rUXcizNaNWAbPNfaNQN8r8UeMR0Ufu99y\ncIVulwm2X0t3N9ER0FGr/J1E77aizivCd0wfc8ZjPcfq8J71duTZbye/Zra747LlZUsRFVYCsV18\nDEmhbsukxogRIxRDUkhExUhMrJ/I1V++2ufxNOPzM1jwpwVsanB2HkFvJAublmIzwnz+hOelZJEk\nvIpaFCOqQHYon8db7U7HS3/t9lGHME2bgHAaEtthmWwhu4JFkFkXsn1E2VPbIs5bO0XQk8jNShtE\nrblnOeLZNBb/jqmBfGM1yM6qlfy4lApEAGw0/byBZIoN1tu2WXet2s3xiI4RI4aHIendFGaTmPrq\nVO6/6v6ca2ywvSs4UrtTPL/t+fBymevxXF6fRrxzgtHVbrR2BlEhQXgMgPVo6kKY1gjExpBECuu0\nIQFyNiOrLU16lhnTCMQIPQZhtpp8N9YJiDCzEdFu6dQk+Wk/CiXks/SD6cItHRCDcDVenqSM+TsM\nv/utjWH4FF723YpAmxWII0BwTvtMf+3IbqTMjKkcyVtlM+1ONXNP4s98uxepR7EPEnsTdL7WGXs3\nxRhyGFJZYJVS5wD3IT/1+VrrOwPXe/RjCjL+q798daiAiMKsK2ex8PmFZEoy6E7NuNpxHH/s8YxO\njObpdU+T6kqRTqWpppo22iitKCXTkeGQUYcwdtxYdJdmxLgR7N21F1WmGD5quO+4dU9rro07vmNO\nPYYNjRuEyaXxVC0JoBNK06UcPf1oUq0pduzaQbYiS6opBWVQUlVCpjkj91oXWJu6vBNqS2v5+Cc+\nztbNW9nVsouSqhJSe2QeuaC9rLQlYb6JDkQlVYEw+lYoH1UOGtJdaWmTRZh2FaKK2ouMoRwREGmE\nkXdC+YhyyqrKSLelOWTUIYwZNYb1b68nk8hIP23IXGpKyKaycn+Z3F9dXc1RDUehuzRpnaZxdyMT\n6iewbfM2mlPNMuaMjKdmWA11w+oYOXZk3vMOe/afO+tzPf4xKaVGAvORvYsGLtdav+hcn4mINOs6\n9xut9W2BPmIhEWPA0B9CAq31Af+PsJqNyFo5AbwGHB1oow8EVq9e/ZGieyBpH8g5m/erp+/tLxDB\nACLKagPXZwJLu+ljUOY3GM92qNAYLDqDQaM373Xw/8Hi3XQysFFrndRadyJRCQe2jJbBc88995Gi\neyBpH8g59xRKqVrgVK31TwG01l1a65awpoM7snAMxrMdKjQGi86H5X0/WITEoUjGIott5lyMGAcr\npgC7lFI/U0q9qpR6SCkVTMeogU8ppV5XSj2hlPr7AzDOGDH6hINFSMRK2RgfNpQhzsn/V2t9PGKC\nvz7Q5lVgktZ6OjAP8ZuLEeNDhYPCcK2UmgHcorU+x3y+Achqx3itlDrwA40xpKF7YOBTStUDL2it\np5jPpwDXa62DNQfde7YAJ2itm5xz8XsdY0DRk/c6DAdLnMRa4AilVAOS/edL+JNiHBD3xBgxoqC1\nblRKbVVKHam1/itwBuK0nINSqg7YqbXWSqmTkUVZU6Cf+L2OcVDjoBASWusupdRVwFOIp9NPtNZx\n+rUYBzuuBh5WStlKGJcrpa4E0Fr/GPgC8L+UUl2Ic++lB2ykMWL0EgeFuilGjBgxYhycOFgM15FQ\nSp2jlHpLKfWOUuq6XvbxU6XUDqXUG8650UqpZ5RSf1VKPW0Co+y1Gwy9t5RSZznnT1BKvWGu3e+c\nr1BKPWbOv6iUOsycn6SUWq2U2qCUelMpdc0g0q5USr2klHpNKfVnpdTtg0XbXCtVSq1TSi0bZLpJ\npdR6Q/vlwaRdCIbOBtPnI0rl545RSs01/b6ulDqumH57QkMpNVMp1WKezTql1Pd6SsP0M9vQeFMp\nNTuiTV/nUpBGb+aiesgHAvcWzYf6SCfv/e0BjUvM959RSh1f4N6e8dS+BloM5H+KCLIrsp9TgeOA\nN5xzdwHfNcfXAXeY4783dBKG7ka8HdfLwMnm+AngHHP8r4iXC4g95VfmuB74uDkeBrwNHD0YtM3n\nau0Fer0InDKItL+NlCxaOljP23zeAowOfP+DQrvA+9eAyZFrPj8GfC3Q5jzgCXP8CeDFHr7jxdCY\nSTfBfUXQOQZJ5FKJ/D6fAab281yKodHjudADPhC4r0d8qLd0ot7fHtA4CslUtho4PuK+HvPUfmfs\n/fkf+CSwwvl8PeJB0pu+GgIP9C2gzhzXA2+Z4xuA65x2K4AZSGaivzjnLwX+02nzCXNcBuyKGMNi\nxMA5qLSRTEprkPQRA04bmIhkljoNWDaYzxv5kY0JzH/Qv+sA/dHIAmGUuWcZcEagzX8CXwobc5Hv\ndzE0Ztrvow+/yS8gaXPs5+8B3+nnuRRDo1dzoUg+ELinx3yoN3Si3t9iaTjnCwmJHs/lYFc3DWSQ\nXZ3Weoc53oHkWAVJ+7YthGbw/PvOWHLj1Fp3AS1KqdEuMSWeW8chFZ0HhbZSqkQp9ZqhsVprvWGQ\naP8I+A7+HLOD9bw1sFIptVYp9S+DTDsUWjya7gHeQ7z3mrXWwSoWYe/6xEL99oKGpu/BfW8Cpxr1\nSTVSVzA4zj7NpUga/TEXiH43XPQHHyqGDoS/v/2JHs/lYBcSelCIiEgdMFpKqWHAb4DZWuvWwaKt\ntc5qrT+O/MD+QSl1WuD6QNA+C3H7XEdESooBft6f1lofB5wLfEspdeog0g6FUmoq8L+Rld8hwDCl\nVFjl8ODzKnqcRdLoc3Cf1vot4E4kB/KTSJL1sNJRvZ5LkTT6PVCxwLvRr+9LN+9gwfe3P8j39IaD\nXUi8D0xyPk/Cv8LrC3YoCYhCKTUBKZkTRnOiofk+/tWMPW/vmWz6sonemsznBCIgfqm1ti/yoNC2\n0JJTaDlwwkDTRhKpX6gkcOxR4LNKqV8O1py11tvN311IcvSTB4t2AZwI/Elr/YHZffwWSZDuImws\n73fTb49oaK1btdZt5vhJINHdLigMWuufaq1P1Fp/Bqni8XagSV/n0i2N/poL0e+Gi/7gQ8XQiXp/\n+xM9nsvBLiRyQXZKfNG/hBSk7A8sBb5mjr+GtxJZClyqlCpXSk0BjgBe1lo3AnuVUp9QSingMiQN\ndLCvL2DKB5l2PwH+rLW+b5Bpj7UeFEqpKuBMZEU2oLS11jdqrSdpiUS+FFiltb5skOZcrZQabo5r\nkF3NG4NBuxu8BcxQSlWZ/s4A/hxosxT4ZzP2GYi6aAfFo1saSqk6cw0VEdxXDJRS483fyUi5rEf6\neS7d0uivuRD9brjoDz7ULZ0C729vEBWk2fO5FGMgOZD/kW3X24hF/oZe9vEooqdNI/q4byCGvpXA\nX5Ft7Uin/Y2G3lvA2c75E8yXthGY65yvAB4H3kG8iBrM+VOQbfJrCINeh1R2HgzaxyJb8teQUknf\nMecHnLZz/TN43k2DMecpZr6vIXrtGwZ7zgXewe8iEdlvICnGy4ErgSudNg8Yeq8TYXjsCw3gW+a5\nvIbUVZzRy9/T84bOa8Bp5lx/z6Ugjd7MhR7wAURlt7w3fKi3dJDSYnnvb5E0LkfKdW1Fqsg0Ak/2\ndS5a6ziYLkaMGDFiRONgVzfFiBEjRowDiFhIxIgRI0aMSMRCIkaMGDFiRCIWEjFixIgRIxKxkIgR\nI0aMGJGIhUSMGDFixIhELCRixIgRI0YkYiERI0aMGDEi8f8Bhdf5Z45f0qQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x121303dd0>"
       ]
      }
     ],
     "prompt_number": 1084
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#multilinear regression OLS\n",
      "\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import statsmodels.api as sm\n",
      "\n",
      "X = loan_limit_by_inc[['annual_inc','emp_length_clean','grade_clean']]\n",
      "y = loan_limit_by_inc['funded_amnt']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1085
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = sm.add_constant(X)\n",
      "est = sm.OLS(y,X).fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1086
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "est.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>       <td>funded_amnt</td>   <th>  R-squared:         </th>  <td>   0.212</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.212</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1495.</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Wed, 19 Nov 2014</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>18:25:21</td>     <th>  Log-Likelihood:    </th> <td>-1.6328e+05</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td> 16683</td>      <th>  AIC:               </th>  <td>3.266e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td> 16679</td>      <th>  BIC:               </th>  <td>3.266e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>      <td> </td>     \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>const</th>            <td> 2194.4996</td> <td>  212.309</td> <td>   10.336</td> <td> 0.000</td> <td> 1778.351  2610.648</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>annual_inc</th>       <td>    0.2503</td> <td>    0.004</td> <td>   60.796</td> <td> 0.000</td> <td>    0.242     0.258</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>emp_length_clean</th> <td>   78.6857</td> <td>    9.810</td> <td>    8.021</td> <td> 0.000</td> <td>   59.456    97.915</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>grade_clean</th>      <td> -478.7410</td> <td>   27.957</td> <td>  -17.124</td> <td> 0.000</td> <td> -533.541  -423.941</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td>197.206</td> <th>  Durbin-Watson:     </th> <td>   1.967</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 137.111</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td> 0.106</td>  <th>  Prob(JB):          </th> <td>1.69e-30</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 2.609</td>  <th>  Cond. No.          </th> <td>2.43e+05</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1087,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:            funded_amnt   R-squared:                       0.212\n",
        "Model:                            OLS   Adj. R-squared:                  0.212\n",
        "Method:                 Least Squares   F-statistic:                     1495.\n",
        "Date:                Wed, 19 Nov 2014   Prob (F-statistic):               0.00\n",
        "Time:                        18:25:21   Log-Likelihood:            -1.6328e+05\n",
        "No. Observations:               16683   AIC:                         3.266e+05\n",
        "Df Residuals:                   16679   BIC:                         3.266e+05\n",
        "Df Model:                           3                                         \n",
        "====================================================================================\n",
        "                       coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------------\n",
        "const             2194.4996    212.309     10.336      0.000      1778.351  2610.648\n",
        "annual_inc           0.2503      0.004     60.796      0.000         0.242     0.258\n",
        "emp_length_clean    78.6857      9.810      8.021      0.000        59.456    97.915\n",
        "grade_clean       -478.7410     27.957    -17.124      0.000      -533.541  -423.941\n",
        "==============================================================================\n",
        "Omnibus:                      197.206   Durbin-Watson:                   1.967\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              137.111\n",
        "Skew:                           0.106   Prob(JB):                     1.69e-30\n",
        "Kurtosis:                       2.609   Cond. No.                     2.43e+05\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 2.43e+05. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 1087
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Mapping using folium"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_geo = r'https://gist.githubusercontent.com/datadave/108b5f382c838c3963d7/raw/3036216d894d49205948dbbfd562754ef3814785/us-states.json'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1088
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = loan_2[['addr_state','funded_amnt','annual_inc','emp_length_clean','loan_status_clean','grade_clean']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1089
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df[df['annual_inc']<200000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1090
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import folium\n",
      "from IPython.display import HTML"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1091
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 53487 entries, 3 to 188121\n",
        "Data columns (total 6 columns):\n",
        "addr_state           53487 non-null object\n",
        "funded_amnt          53487 non-null float64\n",
        "annual_inc           53487 non-null float64\n",
        "emp_length_clean     53487 non-null float64\n",
        "loan_status_clean    53487 non-null int64\n",
        "grade_clean          53487 non-null int64\n",
        "dtypes: float64(3), int64(2), object(1)"
       ]
      }
     ],
     "prompt_number": 1092
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [2, 3, 4, 5, 6, 7] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'grade_clean'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Grade (G-1          F-2             E-3             D-4             C-5             B-6            A-7)') # legend\n",
      "map.create_map(path='grade3_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1093
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=grade3_chloropleth.html width=1000 height = 500><iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=grade3_chloropleth.html width=1000 height = 500><iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1094,
       "text": [
        "<IPython.core.display.HTML at 0x1211b78d0>"
       ]
      }
     ],
     "prompt_number": 1094
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [5000, 10000, 20000, 30000, 35000] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'funded_amnt'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Funded Amount') # legend\n",
      "map.create_map(path='funded_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1095
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=funded_chloropleth.html width=1000 height = 500><iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=funded_chloropleth.html width=1000 height = 500><iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1096,
       "text": [
        "<IPython.core.display.HTML at 0x113822e90>"
       ]
      }
     ],
     "prompt_number": 1096
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.annual_inc.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1097,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x119c7b2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jwLOS1kgScD3wldpjpp7rA8BDi/yycuvH3D0ArJU0IunVwFV075FqnfQmN+X9\ndM9P8HzOKb3+24D9EfFntV3lnaO5P4Uwj08pvI/uJxMOA1tyx1PKD91lgr3p51tTc0P3kxUPAgfT\nyTBSe8xn0jweAH671r+a7i/4YeCWWv/5wJ3AIeBRYDT36+7h/O2i+w0Jv6S7Lvyhfs1dGutQ+tmY\ney4WaT4/TLeI+03gCbpvdss8n/Oez3cAz6ff78fTz9UlnqO+2dDMzBorfTnLzMwK5iRiZmaNOYmY\nmVljTiJmZtaYk4iZmTXmJGJmZo05iZiZWWNOImZm1tj/B/ssO6nqtBQrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11774c650>"
       ]
      }
     ],
     "prompt_number": 1097
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [20000, 40000, 60000, 80000, 100000, 120000] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'annual_inc'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Annual Income') # legend\n",
      "map.create_map(path='income_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1098
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=income_chloropleth.html width=1000 height = 500><iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=income_chloropleth.html width=1000 height = 500><iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1099,
       "text": [
        "<IPython.core.display.HTML at 0x11709ca90>"
       ]
      }
     ],
     "prompt_number": 1099
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [1, 2, 4, 6, 8, 10] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'emp_length_clean'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Employment Length (years)') # legend\n",
      "map.create_map(path='emp_lengthh_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=emp_lengthh_chloropleth.html width=1000 height = 500><iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=emp_lengthh_chloropleth.html width=1000 height = 500><iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1101,
       "text": [
        "<IPython.core.display.HTML at 0x11709c290>"
       ]
      }
     ],
     "prompt_number": 1101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Doing some cleaning on loan status to determine the average status per state"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = df.groupby(['addr_state']).loan_status_clean.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.to_csv(\"/Users/olehdubno/Desktop/python_tests/df1.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = pd.read_csv(\"/Users/olehdubno/Desktop/python_tests/df1.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map = folium.Map(location=[40, -100], zoom_start=4) # Initialize map\n",
      "\n",
      "thresh = [0, .2, .4, .6, .8, 1] # set the threshold, use histogram as guide\n",
      "\n",
      "map.geo_json(geo_path=state_geo, data=df,\n",
      "             columns=['addr_state', 'loan_status_clean'], # pick columns\n",
      "             key_on='feature.id',\n",
      "             threshold_scale = thresh, # set threshold\n",
      "             fill_color='YlOrRd', fill_opacity=0.75, line_opacity=0.5, # colors\n",
      "             legend_name='Loan Status') # legend\n",
      "map.create_map(path='loan_status1_chloropleth.html') #draw map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "'addr_state'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-1107-d783f0282cc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m              \u001b[0mthreshold_scale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthresh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# set threshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m              \u001b[0mfill_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'YlOrRd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_opacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.75\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_opacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m              legend_name='Loan Status') # legend\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loan_status1_chloropleth.html'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#draw map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/folium/folium.pyc\u001b[0m in \u001b[0;36mgeo_json\u001b[0;34m(self, geo_path, data_out, data, columns, key_on, threshold_scale, fill_color, fill_opacity, line_color, line_weight, line_opacity, legend_name, topojson, reset)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;31m#Create DataFrame with only the relevant columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;31m#Save data to JSON\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1682\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1683\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1689\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1691\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1053\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2536\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2537\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2538\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2539\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1154\u001b[0m         \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m         \"\"\"\n\u001b[0;32m-> 1156\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3650)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3528)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11908)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/olehdubno/anaconda/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11861)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: 'addr_state'"
       ]
      }
     ],
     "prompt_number": 1107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=loan_status1_chloropleth.html width=1000 height = 500><iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=loan_status1_chloropleth.html width=1000 height = 500><iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1108,
       "text": [
        "<IPython.core.display.HTML at 0x1171a6710>"
       ]
      }
     ],
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