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odubno/Loan Data (2007-2011) from Lending Club

Created November 12, 2014 23:29
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Loan Data (2007-2011) from Lending Club
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Loan Data (2007-2011) from Lending Club
{
"metadata": {
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"signature": "sha256:80eea1f25dfe499f729f79fd21fd1cf6a68ecb92a99c5b6556f2b306adffd130"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Loan Data (2007-2011) from Lending Club"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I used Lending Club to extract loan information. \n",
"\n",
"See the data at https://www.lendingclub.com/info/download-data.action"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###What's a good predictor for whether someone will pay off their loan or not? \n",
"My prediction is that the length of employment, the amount funded and/or annual income will help to determine loan status."
]
},
{
"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": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Import CSV file"
]
},
{
"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": 2
},
{
"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": 3
},
{
"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": 4,
"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": 4
},
{
"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: 188127 entries, 0 to 188126\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": 5
},
{
"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": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be focusing on determing the features that cause people to default on their loans.\n",
"\n",
"The loan status column has 7 items. We will leave Current and Fully Paid. The rest of the columns will be grouped as Unpaid. \n",
"\n",
"Which features are representative of a person not paying their loan on time?"
]
},
{
"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": 7,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10db3e0d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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4iv0P4FvAL22fUGuwMbTt56SbpI8Dfwn8DFgMvAJ4j+1/qjVYTdIpVKQ86vtT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\niA3SKURExAbpFCIiYoP/D55gGwZRKhIwAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1078d1bd0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Cleaning Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Using the map function to numerate the items of the argument sequence for loan status and grade. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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['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": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Majority of my data is in Current. However, that's not our focus, we want to focus on paid and unpaid. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.loan_status_clean.value_counts().plot(kind='bar',alpha=.30)\n",
"plt.xlabel('Current Paid Unpaid')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.text.Text at 0x1080ef750>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Q0nZXpv4nJb0xfXr4Q8CfpbPS35zE529jG/nQ3jeAEyW9RtL9bfE7p9pQ+kn6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\n",
"text": [
"<matplotlib.figure.Figure at 0x1080c48d0>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Removing the Current Status '2' column. Interested in determing what's responsible for people paying off or defaulting on their loans. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing the \"Current\" status information will gravely limit our data. However, it'll still be pretty good granted we'll have around 54,000 rows still left."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2 = loan_2[loan_2.loan_status_clean != 2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"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": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Cleaning up the Employment Length Column and removing '<', '+', 'years' and 'year'."
]
},
{
"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": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.emp_length_clean.unique()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"array(['10 ', '2 ', '5 ', '1 ', '9 ', ' 1 ', '8 ', '0', '7 ', '4 ', '3 ',\n",
" '6 ', nan], dtype=object)"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Converting my already cleaned employment length column to a float."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2['emp_length_clean'] = loan_2.emp_length_clean.map(float)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Substituting mean values for NaN in relevant columns."
]
},
{
"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": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Running 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": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_Variables = ['funded_amnt', 'annual_inc', 'emp_length_clean', 'grade_clean']\n",
"X = loan_2[X_Variables]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = X.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model = clf.fit(X,y)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model.score(X, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 22
},
{
"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> funded_amnt</td>\n",
" <td> [-1.49218373653e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> annual_inc</td>\n",
" <td> [2.02376963817e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> emp_length_clean</td>\n",
" <td> [2.93841504621e-08]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> grade_clean</td>\n",
" <td> [5.18210343946e-08]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" 0 1\n",
"0 funded_amnt [-1.49218373653e-05]\n",
"1 annual_inc [2.02376963817e-05]\n",
"2 emp_length_clean [2.93841504621e-08]\n",
"3 grade_clean [5.18210343946e-08]"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Train test split"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.25)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.fit(X_train,Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_train,Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"0.78036948106042048"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_test,Y_test)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"0.77662624035281147"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Decision Tree"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import tree\n",
"clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3,min_samples_leaf=5)\n",
"clf = clf.fit(X_train,Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"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:0.780 \n",
"\n",
"Classification report\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 8964\n",
" 1 0.78 1.00 0.88 31850\n",
"\n",
"avg / total 0.61 0.78 0.68 40814\n",
"\n",
"\n",
"Confusion matrix\n",
"[[ 0 8964]\n",
" [ 0 31850]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/Users/olehdubno/anaconda/lib/python2.7/site-packages/sklearn/metrics/metrics.py:1905: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [0]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [0]. \n",
" average=None)\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to the confusion matrix, 9,074 of the loans are predicted as unpaid and 31,767 of loans are predicted as paid. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##KFold"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating a kfold function "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import KFold"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import cross_val_score, LeaveOneOut\n",
"from sklearn import metrics\n",
"from scipy.stats import sem\n",
"\n",
"def kfold_cv(X_train,Y_train,clf):\n",
" # Perform Leave-One-Out cross validation\n",
" # We are performing 1313 classifications!\n",
" kf = KFold(X_train[:].shape[0], n_folds = 10)\n",
" scores=np.zeros(X_train[:].shape[0])\n",
" for train_index,test_index in kf:\n",
" X_train_cv, X_test_cv= X_train[train_index], X_train[test_index]\n",
" y_train_cv, y_test_cv= Y_train[train_index], Y_train[test_index]\n",
" clf = clf.fit(X_train_cv,y_train_cv)\n",
" y_pred=clf.predict(X_test_cv)\n",
" scores[test_index]=metrics.accuracy_score(y_test_cv.astype(int), y_pred.astype(int))\n",
" print (\"Mean score: {0:.3f} (+/-{1:.3f})\").format(np.mean(scores), sem(scores))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kf = KFold(X_train[:].shape[0], n_folds = 10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Our kfold model predictive accuracy is 77.9%"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kfold_cv(X,y,clf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Mean score: 0.779 (+/-0.000)\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y.mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###ROC and Area Under the Curve (AUC)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import RandomForestClassifier"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"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. 1.]\n",
"True-positive rate: [ 0. 1.]\n",
"Thresholds: [2 1]\n",
"Figure(800x320)\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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PrH97EUmUclgBGzo0XEQ1cyZ07px0NCLxyqQo6+nuN6Y+YGb3AH+J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\n8P8D3X+QSyGEfK0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112825cd0>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Running the Logistic Regression against Home Ownership"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Creating dummies for home ownership to better understand if one status of home ownership is more predictive of someone paying back their loan."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.home_ownership.unique().tolist()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"['RENT', 'MORTGAGE', 'OWN', 'NONE', 'OTHER', nan]"
]
}
],
"prompt_number": 37
},
{
"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": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_Variables_2 = ['RENT', 'MORTGAGE', 'OWN', 'NONE', 'OTHER']\n",
"X_2 = loan_2[X_Variables_2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_2 = X_2.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_2 = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_2 = clf.fit(X_2,y_2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_2.score(X_2,y_2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 44
},
{
"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> RENT</td>\n",
" <td> [0.0310973077512]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> MORTGAGE</td>\n",
" <td> [0.372819013643]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> OWN</td>\n",
" <td> [0.135355977702]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> NONE</td>\n",
" <td> [0.290465054095]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> OTHER</td>\n",
" <td> [-0.794573804391]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
" 0 1\n",
"0 RENT [0.0310973077512]\n",
"1 MORTGAGE [0.372819013643]\n",
"2 OWN [0.135355977702]\n",
"3 NONE [0.290465054095]\n",
"4 OTHER [-0.794573804391]"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'OTHER', I suppose is when someone is unsure whether they rent, own or have a mortgage. Not suprisingly that's the category that has a higher coefficient predicting whether they'll default on the loan. Don't give a loan to someone that doesn't know their home ownership situation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Running the Logistic Regression against years of employment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Printing out the contents of the column and creating dummies for better logistic regression. We would want to know they years of employment that could be predictive of someone not paying 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": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.emp_length.unique().tolist()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"['10+ years',\n",
" '2 years',\n",
" '5 years',\n",
" '1 year',\n",
" '9 years',\n",
" '< 1 year',\n",
" '8 years',\n",
" 'n/a',\n",
" '7 years',\n",
" '4 years',\n",
" '3 years',\n",
" '6 years',\n",
" nan]"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_Variables_3 = ['< 1 year','1 year','2 years','3 years','4 years','5 years','6 years','7 years','8 years','9 years','10+ years']\n",
"X_3 = loan_2[X_Variables_3]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_3 = X_3.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_3 = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_3 = clf.fit(X_3,y_3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_3.score(X_3, y_3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.DataFrame(zip(X_Variables_3,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> &lt; 1 year</td>\n",
" <td> [-1.55742386533e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1 year</td>\n",
" <td> [2.04184821527e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2 years</td>\n",
" <td> [8.08572746962e-06]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3 years</td>\n",
" <td> [1.3742030529e-05]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
" 0 1\n",
"0 < 1 year [-1.55742386533e-05]\n",
"1 1 year [2.04184821527e-05]\n",
"2 2 years [8.08572746962e-06]\n",
"3 3 years [1.3742030529e-05]"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Lets see some graphs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected only the people without Tax Liens qualify for loans. Not surprisingly."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(18,6))\n",
"\n",
"ax1 = fig.add_subplot(121)\n",
"loan_2.loan_status_clean[loan_2.tax_liens == 1].value_counts().plot(kind='barh',label='Tax Liens')\n",
"loan_2.loan_status_clean[loan_2.tax_liens == 0].value_counts().plot(kind='barh',color='#FA2379',label='No Tax Liens')\n",
"\n",
"\n",
"ax1.set_ylim(-1,2)\n",
"plt.legend(loc='best')\n",
"plt.ylabel('Paid Loan Unpaid Loan')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"<matplotlib.text.Text at 0x110bf93d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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kiZIDzFJVUbJEyQGxspTFwS9JUhux45ckqYLs+CVJUsMc/AWJ1CtFyRIlB5il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\n",
"text": [
"<matplotlib.figure.Figure at 0x110bb9ad0>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to our boxplots a loan is more likelier to be paid off if the person is making $20,000 more on average in annual income.\n",
"\n",
"And interestingly the amount of the loan doesn't really effect whether it'll be paid off."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.boxplot(column='annual_inc', by='loan_status_clean',grid=True).set_ylim(0,100000)\n",
"\n",
"loan_2.boxplot(column='funded_amnt', by='loan_status_clean',grid=True).set_ylim(0,40000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"(0, 40000)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x110b74bd0>"
]
},
{
"metadata": {},
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Jui7V3y3piLF8gVZZtV9Sb9Zo7rP1N5IjiquA+SV1S4H1EfF64M60jaTZwJnA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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11631bc90>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we just plot a scatter, there's not much of a relationship between income and the amount funded. However, deep down we know there is. "
]
},
{
"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')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"<matplotlib.text.Text at 0x116634510>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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XtXBsfl95+QifOXPmdtfu02fItufl5SN8xYoVO6xzefmINh/fXueK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\n",
"text": [
"<matplotlib.figure.Figure at 0x11663ee90>"
]
}
],
"prompt_number": 57
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several outliers skewing our data. Lets limit our scatter plot by annual income. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(10,5))\n",
"plt.scatter(loan_2['annual_inc'], loan_2['funded_amnt'])\n",
"plt.xlim([0,100000])\n",
"plt.title(\"Plotting Annual Income against Funded Amount\")\n",
"plt.ylabel('Funded Amount')\n",
"plt.xlabel('Annual Income')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"<matplotlib.text.Text at 0x1165e4910>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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9i8Wy5PMlIzh62rY2yedLUqksk0pleUgD2KjmzJbv0LvXdXnj+fE0nSLh6Oj4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eEks9sZINx0Y4vZv87xpxEXOpwrmIleuFOFxDTPDIm7EmftTS6ilZbzEhKWWHB2oTJVwueWEQ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\n",
"text": [
"<matplotlib.figure.Figure at 0x116400cd0>"
]
}
],
"prompt_number": 58
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool. According to our scatter plot, if we set our limit on income to 100,000 we notice that at about 70,000 the funded amount stabilizes. There's a positive linear correlation between annual income and the amount funded."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(10,5), dpi=1600)\n",
"a = .65\n",
"\n",
"loan_2.groupby(['addr_state']).loan_status_clean.sum().plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 59,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1167a3f10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AJUuWMDU1BWxpruvqeGI1MFUZrkwJ5nf26zu9fvVxQvjTuMY1rvN73Me3MD0e\ny1/XxqeHN2zYwEC4e98/wEj5VO+slb8VeFUeXkZqrYKUBL8W2BHYB/gBW1rOLgcOzXVeBBzRsDwf\nlFWrVg2sjaIHHDz/raoM91/vCN7b6idpXYfVR/ISTR/JS2l9JC+D6nV+d8PLsPuplJ9R1D1qfd6W\nPeOoQVq0Hgv8JfBdM7syl50KvBk438xOIL/eIUdJ68zsfGAdcAdwYjYCcCLp9Q47k17vsHKgaFAI\nIYQQooPoW4cjZpL6yydpXYWYNHR+dwPtp/LoW4dCCCGEEPNEpwOtbRP9uqWvJ47OZd3R9JO0rm30\nkbxE00fyUlofycswep3f8b3kOYrW3+VtU1Lf6UBLCCGEECIyytEaMZPUXz5J6yrEpKHzuxtoP5VH\nOVpCCCGEEPNEpwOtSH2ww+gnKa9hktZVeQpzo4/kpbQ+kpdh9Dq/43vJcxStv8vbRjlaQgghhBAd\nRDlaI2aS+ssnaV2FmDR0fncD7afyKEdLCCGEEGKe6HSgFakPdhj9JOU1TNK6Kk9hbvSRvJTWR/Iy\njF7nd3wveY6i9Xd52yhHSwghhBCigyhHa8RMUn/5JK2rEJOGzu9uoP1UnplytHYYpRkhhBDDkX4w\nm9EPphBx6XTXYaQ+2GH0k5TXMN/ramY9/+bDzyjq7ro+kpfS+sG1nv9WVYbn1ssw+vk+v6PqI3nJ\ncxStv8vbRjlaQswJw/1ICSGEEMOiHK0RM0n95ZHWNZIXIYYh2jEczY9oRvupPHqPlhBCCCHEPNHp\nQCtSH+ww+knKa4i0rspT6IY+kpfS+i4fw3mOYnV3WR/JS56jaP1d3jYl9XrqUAghhBBD0euBInVL\nbkE5WiNmkvrLI61rJC9CDEO0YziaH9FM6f3UXP9kHQN6j5YQQghRQe8kE6NEOVrzqJ+kvIaZ1nWU\n77lSnkI39BG8jOq47PIxnOcoVnc5ffl3ksVZ1z/MEab+aNumpL7TgZYYN/SeKxERHZdCiOFRjtaI\nmaS8hjbrOj95BHNXvxhPIh03kbzAMOd3M9G9dx3laJVHOVpCCCEC0PxjL8S40+muw0h9sMPoJ6k/\nu11uQLu6o+UpdDmHI5I+kpc8R7H6I3kZRh/p/I60LSfpGG5bf7RtM685Wmb2YTPbbGZXVcp2M7NL\nzOxaM7vYzBZUpp1qZteZ2XozO7xSfrCZXZWnvWvw1RFCCCGE6CYz5miZ2eOBXwLnuvuBueytwP+4\n+1vN7FVK7lcwAAAf0klEQVTAvdx9mZkdAJwH/DFwf+CLwGJ3dzNbA7zU3deY2UXAu919ZcPylKM1\nJihHS3SdSMdNJC8Q6/xuSzQ/JVGOVnlm/a1Dd/8K8PNa8XOAc/LwOcBRefhIYIW73+7uG4DrgUPN\nbA9gV3dfk3XnVubpNLN5/FsIIYQQ482wOVoL3X1zHt4MLMzDewIbK7qNpJatevmmXD4r4vTBDvv4\n9+B+4qzrcPpIORyRckSi7adI+khe8hzF6o/kZRh9pPM70racpGO4bf3Rtk1J/ayfOszdgpPTRiiE\nEEKI4kR7LciwDBtobTaz3d395twt+ONcvgnYq6J7AKkla1MerpZv6lX50qVLWbRoEQALFixgyZIl\nTE1NAVuiyKmpKaamprYar08fhT6xGpjKf6u3WpfZ6rs+vmX96uNspa+UUGf16tUzLq9eX6/ppeof\nVj/o8tvoDzvssG3WcZpVq1bNWl/a//R4F8/v2fgZZHzQ82nSzu+o14NB6yutr60h0/urXP1sNX12\n598qtj2+DpuT+ofVTw8vX76cQRjohaVmtgj4XC0Z/qfu/hYzWwYsqCXDH8KWZPiH5Favy4GTgDXA\nhYxJMnzbREMlYYKS4cuiY3LuiLRtInmBWOd3W6L5KUmXk+G7sp9mnQxvZiuAy4CHmtlNZvZC4M3A\nU83sWuBJeRx3XwecD6wDPg+cWImaTgQ+BFwHXN8UZLWlOZqeP33T3dFc6aOta9lt067u0vup5LaJ\ntd0H00f9/l+0bVlyXSMdw3mOQtpo3nUMz1X90byX1M/Ydejux/WY9JQe+jOBMxvKrwAOHNiZECIw\n0/dPq9nSrK8nbYUQoo6+dThLSnbTdD0RMFLXQleaoOeC0l2H2pagrsNY53dbovkpiboOy6NvHXYe\nfR9MCCGE6CrDvkcrBJH6YPMcBfXt6u72tmlXd5f7+mNt9/L6SNsm0nETycsw+kjnd6RtOUnHcNv6\no3kvqe90oCWEEEIIERnlaM2S8jla8funexFpXbu+LdugHK25I9K6RvICsc7vtkTzUxLlaJVn1q93\nEEIIIYQQw9HpQCtSH2yeo6C+Xd3d3jbt6u5yX3+s7V5GP4p3bo1CHymXJ9IxnOcopI3mPdb5HWld\n29YfzbtytIQQHaf+4XUhhJgMlKM1SyLlaEV771akHI6u9PXPBdFytErmcJQm0nETyQvEOr/bEs1P\nSZSjVR69R2vi0Hu3RHeJdrMguoGOGxGZTncdRuqDzXMU1JesO9q2aVd3l/v6Y233KPp6N+NgP5TR\ntmXXcrRm8w3LGOf3cMdNpP3a5WO4bf3RvCtHSwghxAgYNlgRQvRCOVqzJF6OVpz+7Eg5HNG2TUm6\nnKMVbT9F8hPtHOny+R1pv5ZGOVrlUY6WEB1H+SdCCDEzUa+Vne46LJ13ECsfpmTd0XID2tXd5b7+\naPknsfTt6o51DHcvR2s29Ufar5GulZHyhPIcYeovV3e8HM8JadGa3tCrgak8rCfxhBBCzD29buTV\nAj2ZjH2OVpdzA7qed9DlHI5IdCPnqq1+/I7hrnuJdC1rS7RrZaR3x5V+H2OkHK35Ol+VoyXEEJTu\n69cdrxAiLs3BihiOsc/Rqs0Rqn7laM2FNkZf/3C5gJOWc9VW367uWMewcrRmox3dO73a69tty5J1\nR9uv7fTRvOs9WkJ0gmET1oUQ26LzSYwHytFq1Dcz6vyWrvRP9yLSdx0nKW+pG/rxO4a77qXL17Jo\n18ryeUvNxNmW8Y/JuUQ5WkOh/uluoP0khJhEdO3rEp3uOozWxzvfeQ2z0cfKDShZdzR9ybq7rm9X\nd6xjWDla81N3eX2kHK1o20Y5Ws2oRUsIIeaBqG+xFkLMLcrRCqzvSv90LyKtazf0kbyU1o/fMVy6\n7i7nMXbjmOytb0uX85YmKUdrrm52lKMlhBATi3J55gK1Po4z5c+RkedomdkRZrbezK4zs1cNMX/Y\n96t0La9hNttSOVpzpS9Zd9f17eou/e3TSLk83daXrLuk3mn/uol2XqLlLU1SjlbJfTXSQMvMtgfe\nCxwBHAAcZ2b7t69p+iB/J4Mf8ABrWy4nkr5U3YNvy+qP0mGHHdbyRyrCukbUR/ISTd+u7rVr5/6Y\nH42f9nV3Wx/JS2n9zNrRXVdj6dudH+3qLqXvta9mYtQtWocA17v7Bne/HfgYcOTw1d0yQfooXqZ/\nmE6j3Y9UF9d1FPpIXqLp29V9yy2RvLf1E8t7pP3abX2k6+r866uByste9rKWQWWUdW2/r0YdaN0f\nuKkyvjGXCSGEEGLsGTao7C6jDrTmeItumCB9ybqj6UvWHU1fsu6u69vVvWFDO33pdW3np13d3daX\nrDuavmTdXdeXrDuWfqSvdzCzRwOnu/sRefxU4E53f0tFM/7hrRBCCCHGhn6vdxh1oLUD8H3gycB/\nA2uA49z9eyMzIYQQQggxIkb6Hi13v8PMXgp8Adge+P8UZAkhhBBiXAn3ZnghhBBCiHFBb4afMMxs\nZ2BXd/9xrfx+wG3u/uuCyz7U3S8vVf98Y2Y7Ag8DNjVs3+3d/ffz42z0mNne/aa7+3+NyktpzOye\n7v6LHtP2rq6rme3Wry53/9ksvTxyepCGh4/c/ds1/e7ufvNsljkOmNld8iuHhJhzJq5Fy8ye6+6f\nnGUdD3X37/eY9lh3/9qA9ewNHOPub+sx/e4A7v7Loc1uW+cHgZX1bWBmfwY81d3/tlb+JHf/Uh7e\nx91vqM7j7p9qseyb3H2vAXQ7A89y90/Uyv+hz2zu7v/cwss2x4GZHd+r7ryAc2v6fwXe4+5Xm9k9\ngW8AdwD3Bl7h7udVtN8B/tbdLxvUYw/fjdtmhnn+2N2/2VC+H/DXwH65aB3wwaZj28ze02cR7u4n\n1fRX0/yU8X2B+7r79jX9gcA/kgJVgKuBd7j7d/ssd3re+wBPAG509ysapp/p7q+eqZ6K/h+AX7j7\nh2rlJ5BuUs6qlV/p7gfl4Uvd/clN0/L4BrZslz1JuarTuLs/qMFLL34LXA9c7O53Zv2dpG3306YZ\n3P2wWv2bgauAFcAn3b3vy4TM7NHu/o1+mor2uaR1tcr/ipWtrx1mtqO7/65HXVtdewZY9gPcfeMM\nGiPlCx9HOqcW1qZf1Wd2d/eHV7RHkI6N+jXrz0nH0iU9PDycdP458D13v7qH7rRePrKZf6rptwrw\na9Me7+5fqZW1upbPF10NiDsRaM1wx/god/9Wi7oG+rGv6A9x9zW1sjuBjwAn1oOg+oW1ob77Ac8j\nndx7Ap9293+oaU4ElgF3z0W/BN7i7v/SUN/F7n54i/X5trs/sse0de5+QK/1afjR6LuuDfX33Pb5\nqwFHkLbLU4Gvuvtza5rT6f1hKnf3M2bjxcze21C/Ac8GHtAQHPxhe5nZKcCUux9lZruTgtklFe2h\nwHuA7wCvdPeft/A647ZpmOdhWX8s6UJ/cG36Y4BPAR8Avk161ctBwIuBP3P3r9f0S9n2x3Iad/dz\nZvCziHRMPwV4l7u/pzLtSODtwJuA6UDpYOBU4B/d/TO1ui4EXpUD3D2AK4FvAg8mBYrvrOnbHqff\nBh5d/9HPLZZXuPuBvepvc44M4qvPMQ+pR+JhwO/d/XlZfwrp+nIL8HHS9eW2PvXvQNonxwJPJ90s\nrAA+29S6bWZXkh5ietUAQdnyivfnABdUp7v7C2v6zwNHuftva+WPAC5w9wc2LONg4EHAOne/xsz2\nAl4HHOHuja2q+dg/DjgK2A14aa7/ZzXdoj6r5+5+Y0V7WfZeb8m+L/A5d390rfyewGeBvUnXBAMO\nBP4LONLdb63pX8G2x8HdgBOA+7j73Wr6HwL/Crx9uiU9X5feDuzfcD1odY40YWYPIV9z3P1hlfJW\nv1EN9c5ZQJz1rwRWuPtNPeapL//9pOO9MQYZCHcP/wd8C9itofxwYGPLum5qKNsOeC7wSuAZuexR\nwMXA2gb9VcCZwHXAY2rTrmzQ3wNYSnoI4AfAO0jdS03+XgtcBDyoUvYg4D+B1zXot1neDOu/vs20\nav31ZQ2x7Jtq4wZMkS4INwH/AWwGdhnBMbXNcdBwTPxl3tcfBx4+w7a5CHhhZbzpuNkOOBH4IelT\nVO/Jf+9u0LbeNsA+pODku/mc+R9gUQ/tSlJgWC9/IvD5OdzO+wLLgfWkIO4uDZrvNvkEFgHfbSi/\npjL8auDcPLwrcFWP+nfr9dek77M+V89wHAx8jrQ9f/rU07SNHpy3zRrgE8CSAeq5Kyn4WAHcDJzX\noNkeOIV07furFh5nXFfgDcCl1WM8nwMbSa3tTfrvZb/T19Ubsr+dGvRvAq7Nx/6L8v6/YYjtbaSe\niGrZFX30Tcfke0hBz3a1bftWUit5v+Xfg/Q7cQPwFuB+DZp7ka4dV5GClFOAG0lB5XYN+qGORdIL\nx19OutH5DXA6cOAc1f0Y4N2k4POXpN/QpvP1ZcChwGLggflv0fRfg/4s0jX1q6Tr8X1n8PGPpJbj\nvxhmPdy9Mzla/wqsMrOner5jMLPnk4KdZ8xB/R8g/UitAV6buwj2A17jtbvpzB3u/mozWwl8xMzO\nBV7vufm+gc3AJcBpnpvdc1ddE38FPMIrd5Pu/kMzex7pB+P1Nf09c129WhrqzcE/toZcKTM7BPgx\ns8TMPtdn8r1r4zeRuqs+DLzc3X9lZje4+//2qLvafdXUFXESs8TM7gIcD7wCuBz4c+/RTQz8wsye\nDWwC/oR0dzldx04N+t1IAfyPSS03d0JzLg3tt83XgR1JP6pH5WPmBnff0MP7g9x9db3Q3b9sZh9o\nqP9z9G/Rek5NfyDwGlKLy1uBE7x3jtoOTT7dfUPelnWqXQdPAT6Y9bfl1uY6+7GlpWybxZBuZGr2\nt81dMrOF9OgONbOXk7ZNdRhSV+nQDNJl5LU79lz+AzP7LLAL6YbhoczwMTd3/62ZrSMFL48CtvkO\nbd6HZ5nZJcBlZnY2W7aJu/s9BluzxuW/1sxeC3zBzJ5OupE+i3Q8N/Va/BlwkLv/xlLu203Aw/oc\n8/+HdBy8j3Qz8Tvr8+kXS6kbf0MKWq8G3k/6ZNwbST+8H6/Id23q1upzLXgK6ebtD8eru//ezF5D\nCo6a/NybFFT8BXAu8Ejv0TKey/8mt3BeQuqifoz3bsV5aJ+WIa8fY2b2N6QWpvuRbgJfRGoVPL1h\n/la/UWb2JlLDxw+B80nB2xXuvryHvweQPly6P2nbfRW4DLjMG3Ie3f2UfI4+gdSS+zoz+y5wHvAp\nr7UAu/vbzOw84J1m9iLS8VM95mfscu1EoOXuHzSz3wBfMrOnAscALyHdkW+o62doSlzYUPZo8kFv\nZjuR7uYe7O6NeQ4VX/8vN12/H/iKmf1lD+mppIPybDM7n/Rj2Is7vaHJ3t1/bWZNP1T3JHVt9aJ+\nELwCOD83619BOvgPJgUXxzbM/yAzuyDr9qkFUvs06N/Rx8vba+P/QepSOAZmDNLIfqd/7M8A/i9b\nTt5tfgDbHgeWXj1yEumu+uk+c07I35DuuHYHTnH3H+XyJwMX1up+CenO6O2koGOmPvu222Yz8Eek\n9bof6SLVj355f03B3KNJLQsrSAEo9Nn2pB/1jaSW2EOAQyo/avWg+HYze6BXumIAzOyBbB1UTbPR\nzP6eFOAeRGqhwMx2ofmado236xZ5G3Chpfyo6QDtUbm86fj+EKk1rT5s5CBwmlzn9DFcD8rct80z\n/BV9uoyAem7Og0nn8ZGkloCPA29suqZU5tk7z3MsKV1hBfBsd1/fQ38C6Zr2GuDsPjeYrXH3N5jZ\nr0nd2QBPdvfresh/6+6/yfP9zMyu6xNkAexB6no/Dnivma0Gdm4KkDLnArcCXycFfUtJrTbPd/d6\n0Pop4ANm9vee00nMbFfgXWx7DQb4XdMy3f12M/ttvdzM3g78KalR4OH1YKBBfy/gzaTz9un57/Nm\ndrK7X9owyw3As2gOhpp4L+m8O9ndv5OX2Uvb9jeqVUDsOfXGzO5KOk8fQwr8Pmhmt7h70w3DncBq\nYLWZ/R0p8H1zXuYuDfpNllIW3pjXpXrMzxhodSJHaxozO5rU5Hoj8Ex3/0kP3aJa0fSFbW9gmbs/\no6ZvlXvUNN1SIvUbgZ3dvd5yM62ZvggeS2rmPI2UQ3FtRfMl4Ex3/2Jt3icDr/Vtk1lb963nO/O/\nY0vi8TXAe72WX5C1U32qcnf/ck2/zQ/mDF62I3UPHEe6GCwg/Yhc6H0eAhhkvWfIsaB+Uc6tIT8G\nmo6rpru6vXrdIZrZs939c5XxL5FyF5q28bPc/T8bylttGzNbQLrLPxZ4CKkF7Wn11sus/QnpB7Xp\nCnaMu9+vpt+BLT9SB5ICyRXufk2P9V+aB6cvMPXWx3Mq2qNIQcwb2TqwOZWUG/HpWt0LSQHG7sC/\nuPvFufww4GB3f3tNP8w58vS8/Oo58iZ3/3ybehrqPZ2tt8lWw94nz9DM7kG6ETiBdKf/jvrxlI/h\nq4DPkIIEqCSk1wM5S7lFD8j1rfCGhwka9DcCL6u3+DVoqzcHjweqCdhNraBV/eNI3ZOb++h/Afy/\nHsvYRl+bdydSYHFcXtal7v78mua70+e8pTzJHwEPbApa8/nxBlKQMJ2EvhepRfq1DS1d64Hns+UY\nqP7/qLvvV9PfCfyO5huPbVoSLeVovQ94p7vfkcuW5LIN7n5cTd82j/E+pFzAY9nSqvVCd39Ag7Zt\n3dVrzWGkgOipwF49AuLp+RaQgqw/yX8LSN3qL+wzz8PzOhxNSrVY4e7vqmn+CDibtP+rN9QD04lA\nq9YysYj0Yzh9173ND2Bt3keSdtjzSB8n+qRXEnGz5tekpuBpHkzq82+s38z+zpsT058ALHX3F9XK\nFwML3f2rlbIDSa0hT/BKkrWlJObPkpo/qy1OjyMlSV5dq/tXwOFee9LRzB4H/Mjdf0APLCXm0/Tj\n30N/36xvDHCzppoY/EmfIWm7Nu9dgKeR9tfT3P0+gyxnrhgiMPs+Ken2hlr5i0gX1wcNo+3hbUe2\nbJvD+22brF9IungcR7pA1RP/lzJkcnu+czyO1Dp3uru/t5+XQbCU8PwKYPphjHWkRN7vzEHdS713\nt8OssZZPhA1Rf73L6Czv0WWUA7k/LLs6iYZALl+zvuID/hCY2VPqN4F9tFPZxy6kwB/SdfZ/SWbq\nN2ml9TuTekIeQkrD+HBuQboHqXuy/lRxmwcbDiF1Xf4i1/9EUov090jnSD3RfjV9vv072xtqM7ve\n3R/SUG7Ai939A7XyX7JtD8FPSA/ebNOyb6nL+Dx3/6qlBxCOIV0T7kbqfnt1RbsO+Ovq798M3l8G\nfI30kMt2pBakfgHxB0nXjdtI6T9fB77R5xzZlxRcHUNqmVoBfMzdG3sBcgvj60hB61BPPHYl0FpU\nK5qpheqhpB1zDOlg+QTp6aVeT6G0qr8273QgdzSp+bUpkLsQONVrj6rnaPpMd39WpWwx6U59X7b+\n0fk+DYFTbik5pUfdb3T3Z9fKjdSS9lJS8iXA70kthf9Uv+D20zfdfVufJ7AatEeRnuZ7bx5fw5ac\nltPqF75ey+mj+SW9L2bb3AW2xcyeQeoaeKbnVklL3+/8C1JQtXEY7QDLPdXd39RC/0Bv0crYp56d\ngGeSLlKLSE+RfdjdNzVoW+V0tfTRNl+srb4aOFXnawycrMUTYW2DMtu6y+hsn6HLqC3ZT9O26eVn\nYH2+cXojqRtnupVnb+DfgFc3tPKU1p9PahX6KqmFeIO7n1zfJhX979m6G31nYLo1a6vrh6WnMZ/s\nqQvzCaQu25eSurb3c/c/r9V9COmBnB/l8eNJeUk3kgKzn9b0bQOttvrT2fYYvjfp5u50d19R059C\n+n3dk7SuK9z9yukgpnYcvI6US72Vto+Xd5BapvYnBcSX5b+1wGENAfEXsterSUHW10kPIDRe+/N+\nXUXKfR3k9TFVP1eRgsCv0SMHrLGOLgRaVQZsobqTlBvyUs/vErGUGNyUUzRM/W0DuW+5+6N6TLva\n3f+oMn4hKbi7qqbrFTgNXHcueznpIvPX03cqZvYgUp7ZSt+2a6Gtvk2gdRnppJzeR2tJ+U13A5a7\n+5Nq+mrgVL3owdwETq0DM0tduh8g5cT8H1I+0jOb7qbaaGfw2fRqitLBx7+TutEuAj5ePz4bPP6E\nPjld1daGIbwMXPeQ+laP0tfm7du917Zua99l1DaQa+unTVB5Finn62XTAWLePu8A/rce5IxAf5Xn\nV3NY6p76ZptgpB9m9h13f0Qe/hfgJ54Tw6vTKvq2gdlG4J/pfY7Ur8Ot9H3WazdSK1KvlrxFbGkd\n2oWUUL7CK+kww2izvppz9Sf5f2POlaU0i4dVtAeS3iX3DXf/vzXtO7JmP1Ig9zX6JM8P42ebebsQ\naA0R2ByV9YeSEvY+Qfqu4qI5qr9VIGc9mnGbpg0ROA1cdy5bS3pU+ie18vsCl3jl3U9D6qt3gX2D\nofq6mtl73f2lefhydz+0ab2ikS+UnyadsEd7TtCdrbZPHU2BVung405SYnYTTT/4A+d0DeGlbb5Y\nK31t3hnzorJu4O69tnW3oWSQ2FZvZtcD+3otWd5SvtP3G65NpfWzeg9gPyy9oPcgT12R3yfdmH45\nT7vGK++VymVtA7MfkW5uG/Ftu4Rb6WdYt4G2k5kdRGpNPNBr7xwcRmvD5VztlbWPJeXg3dvd79lD\n2ypwGsbPH/Ah3wsxyj9SP+oFwN6VshsGmO/upAvff5J+JN5Hym+ZVf2kd818nNTi9X5SK8yGPvqP\nkU68evmLSa0D1bLr+9SzzbQ2defybd4D1G9aW33L/fqDPtN+ON/H3QD+f0nKC7iN1OLwq8r4rcNq\nB1hu07vgdiC1PJ5Lym14A+lR9151tNLPcjvdlfTE1v+Qbk7mzMtMdQ+rJ3VFvIGUDnAGcK8+2reT\ncjpfRXo7+EweBq57ltt9xvctDeNnUD1wbZ86tpk2Av3vK+fcbaSvOAx1DjbU/RpSq8gF+RjeLpcv\nBr7WoL+a/E45UlrIEyvTrmnQt31n4Vy9o+0w4Et9pu9AykU7j/TgwsdIucRDa0lP6n6N1EjyT/na\n0O/8O5n0e/xfpKetPwL8LfAIYPs+8y3Idb+e9KT5FcC/zdZP47LmYmeU/qNlYNOjjt1InxvZ5qAZ\ntn4GD+R2J/Ubf5nUnPvPefgbwB41bdvAaeC6s77VCxTb6lvuk/N6rOtLSC0O837szdcfWwdm9b/f\nzzBvkeBjiHXYiZR38gnSCw1fB9x/Lry0rbuNnvaB052kx/6b9lU94G5V95DbvWSQOLCe9FDP8Q3l\nLyC9c2mk+tJ/pNaOPwXuVinbl/S+q7q2bWBWNNAi5R7V/zbmc2X/Bv3hpCcqNwOfIz1BefcedQ+s\nzfovkF64vJz0Cp2Hk3vfeujfmc/tPQdc17aBXCs/TX+d6DqcxtIL5I5ky2Of55Jej3DxfNef+7L/\nnJRz9KSG6Zbr/CNS0/41nr8hWNPtTupa+h1bf47krsCfesOjpYPWnbX1BM8qO7v7DrPRt8HSk3Gf\nIX2zbfq9OY8k/Sge5frYbSusRbL6MPqWXtrmdLVJtG9bd1t9q7yoNpSsO9ffKnl+iBywgfVm9gDS\nO4Z+zdbXsl1I17KtHgAprY+GpU8B7U76VuWvctm+pCCk/vHve/sM73WcpX5RrciBn3qPV+xYeghr\n+vuYfRPC22gr8wycc9UWa5k8Pxd+OhVoVZkpsIle/wzLHjhw6jp5XZ9EOojHel1LUjr4GMLPwDld\nQwZCbfLFWum7TOlAbgg/9fN7nTe/MHMketEt2uRctax3qMBpWD+dDbSEEFvocvARyYsQYn4xs5PZ\nkpx+B6mLdfrJwKu996e8hlnWjIHTXPhRoCWEEEKIEJjZO0nvOvu6u/93gfpbBU5z4UeBlhBCCCEm\ngtKBXOMyFWgJIYQQQpRhu/k2IIQQQggxrijQEkIIIYQohAItIYQQQohCKNASQgghhCiEAi0hRGjM\nbKmZvafHtMY3V7es/xQz23mudEIIUUWBlhCiy2zz2LSZtf0s1Mmkz7bMlU4IIf6AAi0hxLxiZp82\ns2+Z2dVm9uJc9kIz+76ZXU56ueC0dh8z+7qZfdfM3lApnzKzr5jZZ4FreiznbmZ2oZmtNbOrzOxo\nM/t7YE9glZldmnXvM7NvZj+n57KTGnSHm9llZnaFmZ1vZncrsoGEEJ1G79ESQswrZnYvd/957pZb\nAzyN9KHXRwK3AquAb7v7SWZ2AXC+u3/EzE4E3uLuu5rZFPCfwMPc/cYey3ku8DR3/+s8vqu732Zm\nNwAHT3/wtuJne+CLwN+7+9VVnZndB/gkcIS7/9rMXgXs6O6vL7WdhBDdRC1aQoj55mQzW0sKrvYC\nXgCscvefuvvtwMcr2j8BVuThj9TqWdMryMp8F3iqmb3ZzB7n7rf10B1jZlcA3yZ9ePaABs2jc/ll\nZnYl8FfA3n2WLYSYUNrmMgghxJyRW6KeDDza3X9jZquA9Wwd3NiA1fX6MDUA7n6dmR0EPBN4g5ld\nWm+BMrN9gH8AHuXuvzCzfwN26lHlJe7+/AG9CSEmFLVoCSHmk3sAP89B1n6klqKdgSea2W5mdhfg\neRX914Bj8/BftFmQme0B/MbdPwq8HTgoT7ot+5j28yvgVjNbCDy9UkVVdznwWDN7cK77bma2uI0f\nIcRkoBYtIcR8shJ4iZmtA75P6j78b+D0PHwLcGVFfzJwXs6J+ixbP3U4U8LpgcDbzOxO4HbgJbn8\nA8BKM9vk7k/OXYHrgZtIH5+lh24psMLM7pqnvwa4buA1F0JMBEqGF0IIIYQohLoOhRBCCCEKoa5D\nIcRYYWb3Jr2Woc6Tp1/hIIQQo0Jdh0IIIYQQhVDXoRBCCCFEIRRoCSGEEEIUQoGWEEIIIUQhFGgJ\nIYQQQhTi/wcg4+QpctxJQQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11635f110>"
]
}
],
"prompt_number": 59
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I was also curious to find out which State had more loans outstanging. Turns out it's California. We have to be careful to assume that's a bad thing, because CA is a large state and probably gives out more loans then other states. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
"plt.scatter(loan_2.annual_inc, loan_2.emp_length_clean, s=loan_2.funded_amnt/500, alpha=0.005)\n",
"plt.xlabel('Annual Income')\n",
"plt.ylabel('Years Employed')\n",
"plt.xlim([0,100000])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fBJ7mqlAIa6RYDIzH8gxs6CaZl0YZBEul4CmVSiH8kwxNyRiQUNe+05qXhyLl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\nRG1kPIQQQtRGxkMIIURtZDyEEELU5v8B0UFJtR2OmJUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1178ec210>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The transparancy indicates the amount funded. Not the best visualization. However, we could see that there are more people receiving funding that have been employed for 10 or more years than the rest of the people that have been employed for less."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In conclusion, I discovered that not knowing personal home ownership status serves well at predicting who will pay off their loan. There's a good chance that if someone doesn't know, you shouldn't be giving them a loan. \n",
"\n",
"Income serves as a good predictor determing how much funding a person receives. Not suprising. However, still pretty cool to make obvious discoveries, especially when learning. \n",
"\n",
"The larger the loan, the lower the chance of paying it back. Suprise! :)\n",
"\n",
"On average if the person is making 20,000 or more, they're more likelier to pay off their loan. \n",
"\n",
"Moving forward, I would like to dig deeper and do more analysis by state. |"
]
},
{
"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>grade_clean</th>\n",
" <th>...</th>\n",
" <th>2 years</th>\n",
" <th>3 years</th>\n",
" <th>4 years</th>\n",
" <th>5 years</th>\n",
" <th>6 years</th>\n",
" <th>7 years</th>\n",
" <th>8 years</th>\n",
" <th>9 years</th>\n",
" <th>&lt; 1 year</th>\n",
" <th>n/a</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> 5</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</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> 6</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</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> 6</td>\n",
" <td>...</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 28000</td>\n",
" <td> 5 years</td>\n",
" <td> 325000</td>\n",
" <td> Fully Paid</td>\n",
" <td> MORTGAGE</td>\n",
" <td> CA</td>\n",
" <td> 0</td>\n",
" <td> A</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</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> 6</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 28 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"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",
"18 28000 5 years 325000 Fully Paid MORTGAGE CA \n",
"22 6000 1 year 70000 Fully Paid MORTGAGE NC \n",
"\n",
" tax_liens grade loan_status_clean grade_clean ... 2 years \\\n",
"3 0 C 1 5 ... 0 \n",
"12 0 B 1 6 ... 0 \n",
"15 0 B 1 6 ... 1 \n",
"18 0 A 1 7 ... 0 \n",
"22 0 B 1 6 ... 0 \n",
"\n",
" 3 years 4 years 5 years 6 years 7 years 8 years 9 years < 1 year \\\n",
"3 0 0 0 0 0 0 0 0 \n",
"12 0 0 0 0 0 0 0 0 \n",
"15 0 0 0 0 0 0 0 0 \n",
"18 0 0 1 0 0 0 0 0 \n",
"22 0 0 0 0 0 0 0 0 \n",
"\n",
" n/a \n",
"3 0 \n",
"12 0 \n",
"15 0 \n",
"18 0 \n",
"22 0 \n",
"\n",
"[5 rows x 28 columns]"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import urllib\n",
"import urllib2\n",
"import os\n",
"import json"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def geocode(address):\n",
" url = 'http://maps.googleapis.com/maps/api/geocode/json?%s' % urllib.urlencode({'address':address, 'sensor': 'false'})\n",
" geocode = json.loads(urllib2.urlopen(url).read())\n",
" if geocode['status'] == 'OK' and geocode['results']:\n",
" return geocode['results'][0]['geometry']['location']\n",
" elif geocode['status'] <> 'ZERO_RESULTS':\n",
" \n",
" msg = 'When tryint to geocode address %s by reading url %s, received a status != OK: %s' % (address, url, geocode['status'])\n",
" print msg\n",
" raise Exception(msg)\n",
" else:\n",
" return False"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"geocode('CA')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
"{u'lat': 36.778261, u'lng': -119.4179324}"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2['addr_state_clean'] = [geocode(x) for x in loan_2['addr_state']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT\n"
]
},
{
"ename": "Exception",
"evalue": "When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-72-4930ad120833>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloan_2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'addr_state_clean'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgeocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloan_2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'addr_state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-63-74eb4869a6b6>\u001b[0m in \u001b[0;36mgeocode\u001b[0;34m(address)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'When tryint to geocode address %s by reading url %s, received a status != OK: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeocode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.head()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for location in loan_2['addr_state']:\n",
" print location, geocode(location)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"NY "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 40.7127837, u'lng': -74.0059413}\n",
"FL "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 27.6648274, u'lng': -81.5157535}\n",
"TX "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 31.9685988, u'lng': -99.9018131}\n",
"CA "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 36.778261, u'lng': -119.4179324}\n",
"NC "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 35.7595731, u'lng': -79.01929969999999}\n",
"DE "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 38.9108325, u'lng': -75.52766989999999}\n",
"OH "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 40.4172871, u'lng': -82.90712300000001}\n",
"SC "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 33.836081, u'lng': -81.1637245}\n",
"NJ "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 40.0583238, u'lng': -74.4056612}\n",
"MO "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{u'lat': 37.9642529, u'lng': -91.8318334}\n",
"MN When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT\n"
]
},
{
"ename": "Exception",
"evalue": "When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-71-a5fb52cabc77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlocation\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloan_2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'addr_state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-63-74eb4869a6b6>\u001b[0m in \u001b[0;36mgeocode\u001b[0;34m(address)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'When tryint to geocode address %s by reading url %s, received a status != OK: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeocode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: When tryint to geocode address MN by reading url http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=MN, received a status != OK: OVER_QUERY_LIMIT"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import folium\n",
"\n",
"state_loans = json.JSONEncoder(loan_2[['addr_state','loan_status_clean']])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"state_loans."
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"map = folium.Map(location=[48, -102], zoom_start=3)\n",
"map.geo_json(geo_path=state_loans, data=state_loans,\n",
" columns=['addr_state', 'loan_status_clean'],\n",
" key_on='feature.id',\n",
" fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,\n",
" legend_name='Unemployment Rate (%)'\n",
"map.create_map(path='us_states.html')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-69-8f51e82605e4>, line 7)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-69-8f51e82605e4>\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m map.create_map(path='us_states.html')\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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