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@odubno
Last active August 29, 2015 14:08
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Loan Data from Lending Club (2007-2011)
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"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": 339
},
{
"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": 340
},
{
"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": 341
},
{
"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": 342,
"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": 342
},
{
"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": 343
},
{
"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": 344
},
{
"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": 345,
"text": [
"<matplotlib.axes.AxesSubplot at 0x13788aa50>"
]
},
{
"metadata": {},
"output_type": "display_data",
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G3IQNCQDsCbzmK1QN+ChwPdb53ohd4OtrWPE5r67EZttNBb6BzVgr9bMvORX9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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108c4f810>"
]
}
],
"prompt_number": 345
},
{
"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": 346
},
{
"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": 347,
"text": [
"<matplotlib.text.Text at 0x13834b650>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+lZCS+DghB92+7ZcJV65tAzbgF9ZVaZBQM/gW4bnz74A/KKxvn79iTfBfCM/p\nawmpQYDPEtKDjxJShWMd9v00IUX4BOE5+4Mp+DkkSQl9nZACnLU8U5AkSZIkSZIkSZIkSZIkSZIk\nSVPl/wMcLoqjl2jI+gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1370afe50>"
]
}
],
"prompt_number": 347
},
{
"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": 348
},
{
"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": 349
},
{
"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": 350
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.emp_length_clean.unique()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 351,
"text": [
"array(['10 ', '2 ', '5 ', '1 ', '9 ', ' 1 ', '8 ', '0', '7 ', '4 ', '3 ',\n",
" '6 ', nan], dtype=object)"
]
}
],
"prompt_number": 351
},
{
"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": 352
},
{
"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": 353
},
{
"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": 354
},
{
"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": 355
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = X.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 356
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 357
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 358
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model = clf.fit(X,y)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 359
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model.score(X, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 360,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 360
},
{
"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": 361,
"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": 361
},
{
"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": 362
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.fit(X_train,Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 363,
"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": 363
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_train,Y_train)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 364,
"text": [
"0.78029597687068164"
]
}
],
"prompt_number": 364
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf.score(X_test,Y_test)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 365,
"text": [
"0.77684674751929439"
]
}
],
"prompt_number": 365
},
{
"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": 366
},
{
"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 8967\n",
" 1 0.78 1.00 0.88 31847\n",
"\n",
"avg / total 0.61 0.78 0.68 40814\n",
"\n",
"\n",
"Confusion matrix\n",
"[[ 0 8967]\n",
" [ 0 31847]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
"\n"
]
}
],
"prompt_number": 367
},
{
"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": 368
},
{
"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": 369
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kf = KFold(X_train[:].shape[0], n_folds = 10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 370
},
{
"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": 371
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y.mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 372,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 372
},
{
"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": 373
},
{
"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",
"png": 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FmL61YgU8+aQlFxGRCgKdv1wpK7OT2UmTrOtSBZlIZvjdfVkPmACc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\n7P8DBQxbZrl1j9kAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x12e4ad9d0>"
]
}
],
"prompt_number": 374
},
{
"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": 375,
"text": [
"['RENT', 'MORTGAGE', 'OWN', 'NONE', 'OTHER', nan]"
]
}
],
"prompt_number": 375
},
{
"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": 376
},
{
"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": 377
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_2 = X_2.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 378
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_2 = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 379
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 380
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_2 = clf.fit(X_2,y_2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 381
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_2.score(X_2,y_2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 382,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 382
},
{
"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": 383,
"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": 383
},
{
"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": 384
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loan_2.emp_length.unique().tolist()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 385,
"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": 385
},
{
"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": 386
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_3 = X_3.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 387
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y_3 = loan_2['loan_status_clean'].values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 388
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = linear_model.LogisticRegression()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 389
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_3 = clf.fit(X_3,y_3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 390
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_3.score(X_3, y_3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 391,
"text": [
"0.77943365368713136"
]
}
],
"prompt_number": 391
},
{
"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> [-1.51966442806e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1 year</td>\n",
" <td> [2.0332536044e-05]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2 years</td>\n",
" <td> [8.05831027416e-06]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3 years</td>\n",
" <td> [1.36166767322e-05]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 392,
"text": [
" 0 1\n",
"0 < 1 year [-1.51966442806e-05]\n",
"1 1 year [2.0332536044e-05]\n",
"2 2 years [8.05831027416e-06]\n",
"3 3 years [1.36166767322e-05]"
]
}
],
"prompt_number": 392
},
{
"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": 393,
"text": [
"<matplotlib.text.Text at 0x10d271bd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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s+CVJUsUc/CmJ1CtFyRIlB5glr6JkiZIDYmXJioNfkqQaYscvSVIO2fFLkqSK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\n",
"text": [
"<matplotlib.figure.Figure at 0x1374662d0>"
]
}
],
"prompt_number": 393
},
{
"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": 394,
"text": [
"(0, 40000)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ek9avBf50kudZRfxSI8BW6f484AOZOoOZ5T8H/rZFvStYN4A0XsNfAuem5QEi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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10a8733d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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T/TGrznpJhbLZTNxVEKmK9tno1ZvMfhH4BLA7VLYFOA3YCRyD3W//Yaw1cQBY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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x116dbef10>"
]
}
],
"prompt_number": 394
},
{
"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": 395,
"text": [
"<matplotlib.text.Text at 0x1379d70d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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EtZCE7RpkHGEIUp3cCwkaNyNzFYYjhR07kVVPooHnke7lOUAlTp+OZPXqzZw6\n9TmGsYXs7M4sXLiBgwerk5kZTWbmPJKTF/PhhyuYNOmfhTqL+Ph4Ro8ez+HDn5Cc/Auff76TRx6Z\n8Ofj69at47bb7uHAgbdJSVnJzJnJjBo1rlCvqZRSpVFSUhLdu/dh69YxpKb+yooV7ejRox9ut7uk\nm6bKMH8FhW6gFVATWVy3e57HDbyVDarIOZCg0A3UQyambomsXvJ/yNuSiASJ75j7dEZ6+Y8gMfdd\nSNdzU2QYqNs8ziHgAeRtrQNMRuLy34HxQDugIenpLzFjxg+FOos5c+aRkfEAcBVQl4yM15k58/s/\nH1+4cCHZ2SOAHkBtMjMnM3du4V5TKaVKo7Vr12IYTYDhQA1yc5/jwIHDmi1UheLvgWIpwFwkHXUM\nSUMdRbqGj5v7HEJSWR41kQzhIfN+3u2e58QCh5FzigJO5n3xSZMm/Xk/Li6OuLi4wp1NmZGLXPoQ\nJGDb5/PYXqSn3oIM/dyHFKGAFKQ4zPvZyOV2Im9dFNKzbwC7fI63z3wda57X2Ud0dFShziImJhKH\nYx8ul/eYkZHeY0ZGRuJwbCI31/t4eHjhXlMppUqjyMhIcnMPIv9/O4AkcnLOEBERUcItU5cqPj6e\n+Pj4Em2DPwYwVkIikVNIRLEQeA7pq0xCFuJ9Cumb9C006YC30KQBEn2sRqqR1yDBpW+hSXPgPqTA\n5Ea00ORPMlg1HKiIXHIrcBsSP09FgsbLkTg7HrmMR4BZSKB4LzAfCQytyPjEq4CvzZ8rgZ5IPdCH\nQAYOR2tcrt+BvlitlQgJ+YyFC7+jS5cul3weJ06coEWLTiQnd8Hlqk5w8MfMnPkZvXv3BiA1NZWW\nLTtz9GhzsrLq43R+wpQpbzJ48C2X/JpKKVUaud1u+vS5mWXLkkhLiyMs7Bvuu28gr776j5Jumioi\n5bX6uDlSBGI1b9OAV5Exf18jGb4Ezp6S5m9If2UOMu3MQnO7Z0oaJ1J9/KC5Pdg8bmsk6hliHtNX\nAAeF0Uh3bwayioknlZaBd94/j2rI5d3HuXr0bTY7VqsVu91GmzZt6NHjGjp16si6dev45Zdl1KtX\nj2HD7mTdunX88ccBMjMzqFq1GoMGDaRp06aFPpekpCQ+/fRTTp8+Q79+fWnTps1Zj6ekpDB16lSS\nk0/Rq1dPOnfuXOjXVEqp0ignJ4dp06axd+8+2rZtw4ABAzyBhCoHymtQWFoEcFBoQaqL3cik1ZuR\nnvtQpFgVMbD5AAAgAElEQVT7FBIcJiMZRYAxhIR8wfbtm4mNjeXQoUOEh4cTExPj/xNQSimlAkx5\nnpJGlZDcXE/lcV+kh/4XYDRQGUm6JiK978HAWs+zgN/IyrqeBx54AqvVSq1atS4YELrdbr766ite\neuklfvzxx/Puq5RSSqnSRYPCcm7btm1IRnAKMlH1bcgax/9EeuNDkK7iW4B+yLzf7YEoDOM+NmzY\nwvTp00lNTT3v6xiGwaBBdzBq1Gv8/e9JDBx4P88++2KxnZdSSimlipZ2H5dzW7ZsoUWLa5GCkC1A\nJ2SI5gfmbQXyMWgJrAIuM3/vBZzEYtlKaGgDoqN3s379MipXrnzO11m9ejXXXHM7aWlbkazjMYKC\nGnD8+EGiorQCWCmllLoY2n2silyTJk2AVGTewG1IPc5cZHWTLcjnLQzoghSYxJv7zgOWYhgbSEub\nx/HjfXj22fwnn05OTsZmq4MEhACVsdsjSElJKYazUkoppVRR06CwnLPZbEgVcUu8cw62Ac4gxSa5\nwCikO/kBZEqZMGSu8SrmfXC5OrB//5F8X6dt27YYxhakoDwZq/VlKleuQI0aNfJ9jlJKKaVKD+0+\nLucyMzNxOmOQ+H84UBf4A/gPkFXAo1QEUomMDOWWW25h0KCB9OnT56w9jh07xoQJE/jyy5lkZ2dy\nxRWtmDt3OlWrVmXKlCkcOXKUq666ksOHD1/y9Anbtm3j229nEhwcxB133EH16tUL/FyllFKqLNEp\naYpXQAaFc+fOpW/fwchbPQbvpNRXIgUlHyJZwzHI6iaLgSAkazgTCRwzzH2uBJYSHBzDxIkP8Pe/\nPwXA4cOHadasA8nJPZCq5o8IDjb48cfveeihv/H779FkZLTGan0Xu/1ysrOvu+iJVlesWMF11w0g\nK2sEVmsqERFz2bhxJbVq1brwk5VSSqkyRoPC4hWQQeHYsWN5553ZwBvATebW+5CFZl5AFom5AXgE\nmTP8BiQwTEbmLqyHrHlc1XxOCvArdvsB0tJSCAoKYvz4Cbz22mkM423z+DOA52nUyMbhwxGcObMU\nWfVkBLAV6cZOxOGow/HjB4mOjr7geXTq1JPVq+8E7gTAZnuSMWNcvP3264W5PEoppVSppIUmqshF\nRUUjYwrr+mxtiAR3mNuzzN8tQH1zezqy+kklZGxhuLlPAyALi8VOeno6ACdPpmIYDXyOXxdwcfr0\naQyjjnncVGTxGs+4xorY7eGcPn26QOeRkpJ61jnk5tYlMVGLWJRSSqmiokFhOde37w1IMDcW2INM\nO/MvZDm7P5BuYjvQDvgRWZEwGAkKX0G6jhcDS5CA8C0slso0a9bqzwzfzTf3JSjo/5Cs4x7gMez2\nbAYPHogsTT0bWcZ6HfAJcAib7Vlq1ape4EKUwYP7Ehr6FLALWEdo6KsMGdKvUNdGKaWUUl7afVzO\nnTp1ipiYGsCNwBzke4ATyR6mATYkU2jzuR+ELIlnN3/mmEcLwmrNpHv36/niiw/PmrPw44+n8Oij\nf+f06VSCgoK4//67efXVF1m+fDl33fUQJ04cpWXLFpw4kczhw3/QsmUbvvzyPwUOCnNzc3niiaeZ\nOvW/OBxBPPPMeO6//94iuEJKKaVU6aNjCotXQAaFR48epVq1RsAPwJPIuEFPhs0zBc3dwEeAnZtv\n7ssbb7xB/frNycraiyyNl4bT2Yh1634y5z1USimlVHEqiaDQ7s8XU/63adMmJPs3AFn/eBgwDqlC\n/i+yzN1WZD7CLBYtWsbJkycJCqpMVpanACQMh6MGycnJ/j8BpZRSSvmFBoXlnHdFkSCkAvg08BLS\nhZyLrF5SC1n/uCUnT2awf/9+wsNzOHNmMoZxBzALu/0IzZs3L4EzUEoppZQ/aKFJOSeZQpDCka5A\nJnAYmU8wGgkIQSp7awFu9u7dy5Il82nRYgZOZ30aN/6AJUvmExkZ6e/mK6WUUspPdExhOXf48GFq\n1GiEFJE8jWQEqyFJ4heB+UiwuAgpRjFwOCw0a9acvXv3UatWHT7//D1atmxZQmcAbrebWbNmceDA\nATp27EinTp1KrC1KKaWUP+iYQlXk4uPjkephG/AxUlyyHAkGs4BrkXGF2eYzmuFy1WTDhuXAr6Sk\n/Exc3PXs3r2FihUr+r39breb/v2HEB+/j5ycjthsr/LKK0/zwANj/N4WpZRSqjzT7uNy7vPPP0fe\nZs98g+OAL4HWSBcyyByCociqJSORCavTgChgJG53U1avXu3nlov4+HiWLNlGWtpysrLeJj19KY8+\n+hgul6tE2qOUUkqVVxoUlnM9evRAKovdQAwyP+GLwHqk6CQYWfP4JnOfh4GTyMomywEXubkHS2w8\n4YkTJ7BYLkcKZUDGPto4c+bMOfc/efIks2bNYv78+WRlZfmrmUoppVSZp2MKy7mdO3dy+eUtkZEC\n1wAtgG+RiayDgP7Id4PjwN+Bt5Gq5DTgSkJDj9K5czg//jgLq7V4vkMkJSUxYsQDrF69hlq1Ypk6\n9a0/K533799PkybtSE//EuiCzfYa9ep9w++/r/eMt/jT7t276dSpOy5XUwwjhZo1c1i9ehERERHF\n0m6llFKquOjax6rILViwABlTWAtIQoK+iUgmsBay5N0RoA+wHalOHg0kU6nSMiZPHsqCBTOLLSA0\nDIOePQeycGFlTpxYyIYNd9CtWy9OnDgBQO3atZk9+0sqVx6N1RpB8+YL+Pnn2X8JCAHGjHmc5OSH\nSE1dwOnTK9i793JeeeX1Ymm3UkopVd5ooUk517FjRyQo3AvsACoik1V7bEMKTr5GvpBkIKufNMJm\nS2batFm8887H2O2hxMbW4Pnnn6Rp06ZF1r6kpCR++20zLlc8YMUwGuJ2z2TFihUMGDAAgGuvvZZj\nx/ZhGMY5g0GPhIQDuN3dzN8sZGV1Zc+etUXWVqWUUqo880emsBZS4bAV+A140Nw+CTgIbDBv1/s8\nZwKwC4lievpsbwtsMR+b7LM9GPjK3L4KqF3E51BmrVixAghHxuINQ8YNvgbciixv97T5+P3Af5Aq\n5eXALo4dq8/SpZvYuDGJdevG8N13zenQ4Wq2bdtWZO1zOp243dnIOEaAXNzuI4SHh/9l3/MFhABd\nu3YkOPgtwAWcIjR0Cldf3bHI2qqUUkqVZ/7oq65q3jYi0cevyIR4g5FKh7z9e02AL4D2SFnsz0BD\npEJiDTDW/DkPeBNYgEQ0zcyftwIDgSF5jhuQYwojIiI4c6YCEl87zZ8tgKeQwpMXgH8gcfsM4B5k\nLsNEYC1SiRwPtDKPOI6QkE9ZuvR/tG/fvkjaOH78RN5773vS0m7H6fyFFi0yWLZsIXb7xSWyT58+\nTb9+Q1ixYgmGkctdd93De+/9u9i6vpVSSqniUhJjCkui0GQWMrDtKuAMkrbyNQFJZ71s/r4AySru\nR2ZYbmxuHwLEAWPMfZ4FViNd4keAy/IcNyCDwujoaFJSOgILfbaGIBlBT7BkINm1IKTIxG7+dAMO\n4P+QYBHgEeTyzsFud2CzBeFyuXA4rPTt25cpU9656MIOwzCYMWMGK1asoV69WO69916Cg4Mv7YSB\nU6dOERQURGho6CUfQymllCpJgTB5dR1kgrxVSFA4DunTXAc8BpwCqpuPexxEMoYu877HIXM75s8D\n5v0cIAWogLdPMsAtQ9Y97oj0ugcjcxAuRC7rQOAosibyB0AHYDoyd+FbSEDoMm8fIFnGWeTkVCQn\nZzZgIytrKLNm/U529j3MmfPlRbXOYrEwePBgBg8eXOgzBQmElVJKKXVx/NmvFg58AzyEZAjfQwa6\ntUJST3kzhqoIpKSkIG9zXyQY/Ax4ErgCSbq2AJ5HsoRrgeeAY0jsXg152wYB7yNdzA5gJpJtfAlo\njvT4P09ubhQLF37vr1NTSimlVBHyV6bQgUyO9znSfQwyMZ7HR4AnmjiEFKd41EQyhIfM+3m3e54T\nCxxGzimKc2QJJ02a9Of9uLg44uLiLuFUyqJQZMLqO5HAcAKSUPXYi2QBKyLjCSOQuqBsIBLpuZ+A\nBImrkYrlIGCPzzH2AXbCwqKK80SUUkqpcik+Pt5cmrbk+KOv2gJ8ikyS94jP9mpIhhBze3tgKN5C\nkw54C00aIAPfViPVy2uAuZxdaNIcuA8Za3gjWmgCQFhYGOnpuUhmbziSpP0WmYbmbiQYnIa8TYOQ\nSa1rAwlIT34NpEZoJLJ2cgYSc1cGdiO9/3bgc4KDg/jgg9cYPvxOf52eUkopVS6V10KTLsBSYDMS\n2AH8DbgN6To2kDTTvUi/pefxu5B01kN4qyTaAlORMtp5eKe3CUYim9ZI8DkEiWp8BWRQKB+q5kgA\n1x95yyOQuDvNZ896yLBMF94EcmugG/AhNlsGUVGR9OjRg+xsF5mZmdSvX4+goGB2795N3bp1GTz4\nFrp27eqvU1NKKaXKrfIaFJYWARwUgowrdCFzEc5Fpqb5A+lazkXGHB5Gan7cSGAYhKybHInFshPD\nCANcWCx26tatzZQpk+nWrRtFKTc3l9dem8z33y+iRo3K/Otff6du3boApKWl8fTTL7B27Raio52k\np2djszl49NG7uf766y9wZKWUUqrs0KCweAVwUOhEgrxByDzhzyLjAf8B/BOZ/cdAhmFejvTqVzX3\n24hMJWlFkrRbkF778dhsL7Jy5c9FNl8hwNixjzFlyirS08djs20mKupDtm9fT6VKlejatTfr11cg\nM3MoUrO0GniK0NCJzJjxEX369CmydiillFIlSYPC4hWgQaEVGXLZDu+8303MR8ch4wa7IwvHXIVk\nCs8gxSWVzf1uRcYhpiPZwxHm8TIZNmw3n376fpG01TAMQkLCyc7e9+drh4YOYfLka4mLi6NlyzjS\n0xOQANdA5iufAuwiLu5rFi+eXSTtUEoppUpaSQSFutRDwBiBLnWtlFJKqfxoUFjuGUjV8JNIMckA\nZJ7BV5AZgiKBW8yfS5HK4yigFzJ70CRgPrIk3jxkWby5QBo22wuMHTuqyFpqsVgYPXoMoaEDgVnY\nbM8TErKM/v37U79+fVq1akJIyB1IhfRwpA5pK6Gh4xk//t4ia4dSSikViAqSlvwfcE0BtpV2Add9\nbBgGVqsNGVMYBEQjxSQRyByEIMvd5SCrAh5FvidkIgXdwUjRSThSjJIDZGKxOKhbtzZTp75Z5NXG\nnkKTH35YTPXql/HSS89Qp04d4OxCk5iYUNLSsrHZ7FpoopRSqtwpbWMKnUhp6mJkjWGPSGRuwCuK\nr1nFIuCCwtzcXOx2BzIlzUpgJ5Ix3AE8A4xHgsIwJEhMRMYKLkDmG78RyR4+CNwAWGjU6DF+/32N\nn89EKaWUCiylbe3je5E5AqsDv/psPw28XZyNUkXDZrMhQd8gYAUyN/hIoBKyfrGBFI90Q74DzEYm\no440jzAeqVZ2AENwOOyMGvUwa9eu5cyZM7Rt25bIyEiUUkopVfYVJAJ9EJmDpKwLuEwheL5pNEG6\ngJ8CBpqP3INMVr0UaIhMPTMa2IRUKIOsg/xfJMO4ieDgOLp06caqVduw2aoQFHSA5ct/olGjRv47\nIaWUUioAlLZMocebwJVAnTz7f1YcDVJFLQhIRpaHruuzvREyeXU4cMrc1gD4GskcRiDT03QyH6uL\ny5XGypVppKdvAxxYLG8ybNj9rFr1sx/OQymllFLFqSBB4edI2epGpNrAQ4PCMiEEmfNvLzJP4WfI\naoKvIcGgE1nNZAXwb2Rs4ZVAReQtjwUOEBw8kWrVGpCQcC3SnQyG0Yc9e/7t17NRSimlVPEoSFpy\nO9L/WNb7XgO0+9gGVEG6hRsj1cQ2oAeSJfwC+W5gQZbBqw4cN7c1BbYQEeGkZ8+e9OrVlUce+YC0\ntEVABHb7ROLitvPTT9/5/byUUkqp8qy0Tl79G1CtuBuiiosb6IJkBbOQLN+jSIz/PRIIVkSCxruA\nE8DDSDHKL0AoTz31IN988xl33303t93WmaCgWEJDY6lXbwGfffau/09JKaWUUkWuIBFoPNAKqT7I\nMrcZQP9ialNxCdBMoRUZHwgyz+AN5s/rgC+Rt9Vt7tMa2IpkECsCS4Ca3HFHb6ZNm/bnMY8dO0Za\nWhq1a9c2K5yVUkopVZRK2zyFHnH5bI8vumb4RYAGhRZkXKETmZQ6FCk+cSCB3yGkEOVH5K1OQ7qZ\nr0Syi2tp0KAGu3Zt9nvblVJKqUBVWruP4/O5qTIhFOkWTkeGhq5ElqmzIyMDrka6kLuZ+4chFcez\nkXnL7yA1NQ2QFVJeeeV16tdvQ+PGnZgx4xt/nohSSimlilFBgsIzyITVp5HuYzeQWpyNUkXJgXQV\nRyMVxw2RbuKnkAziIiRz+J65/15kFUML0o38G1dccTkAr7/+Js8//xl7977Djh3PMWLEQ/z000/+\nPBmllFJKFZOCBIXhyICzCKQPchCg1QVlhgt4GckY7vXZvhMZW5gBNAMmI8UlTYBJyOxDA4ANdOnS\nGoCPP55OWtobQGegF+npE5g69Wv/nIZSSimlilVBgkJfbmAW0LsY2qKKRRASGKYDjyErF44E3kGm\npslBpqG8B1neLggJEN3AECCHEydOsGDBAvN4v5vHAovlBHa7hcTExAK3JjMzk4MHD5KTk1P4U1NK\nKaVUkSnIAMabfO5bgbbIQLTOxdKi4hOghSYhyPjBXPPm+R5gM3+34J2nMAuZvuYeJHD0zGmIed9q\n7utCPgJLALDbbVx7bU++++6/hISE5NuWb775lmHDRgFOnE4b8+fPpEOHDkV3skoppVQ5UVoLTfoh\nS170BXoiYwsHFGejVFGyItm/qkAC0l08EWiJ1AsFA08CKcA+YBuy/N21yMxDLyBdzyHA6+Z+m5GC\nlb8BseTk3M6SJW7+/vcX8m3F/v37GT58DBkZ8WRkHOHkybfo3XsgLperyM9YKaWUUhevIMvcjSju\nRqjilAtcAbRHVisBGAf8A2iDBIljkS8j1YFbke7jICAKKUpJQTKF95jPb4B8P9iLVDZ/Q0bGsyxb\n9la+rfjtt99wONoiU14CDCQraxyHDx+mdu3aRXOqSimllLpkBckU1gK+Q5a6OAF8C9QszkapomTH\nW1HsmXt8MZL9+wHJFC4yt2eZ+12OdBGfBhKRADEIWGbudwZYhQSbPwK1sNsX07Bh/sFd7dq1yc7e\nbB4P4Dfc7tNcdtllRXCOSimllCqsggSFU4A5SBqpOrI22pSLeI1aSBSyFZkY70FzewXgJ6QM9kdk\nzhSPCcAuYAeSkvJoC2wxH5vssz0Y+MrcvgrQ1NOfspFM3x9AC+B6YAxSSH43kgm8ExlL2ACZ4Hqh\nebMjiWIXUpncGxlLWB/JML4HrCMs7BDVq8/l1Vefz7cVzZo14+GH7yU0tCWRkX0JDe3BRx+9T2ho\naNGfslJKKaUuWkEGMG5CBqBdaFt+qpq3jcj0Nr8CNyKVDInAK8igthhk8rwmwBdIf2cN4Gdkcj0D\nWZNtrPlzHvAmsAC4H5lX5X6k/3MgUjrrK0ALTZzIZc9CLlF3YDeS9ZsLHEUuWyLQHFnv2AocBBpS\ntWo09evXJzQ0lOjoaOrWrUvFihU5c+YM4eHhNGnSBIfDQZcuXQgLC7tgezZt2kRCQgLNmjWjfv36\nxXHKSimlVJlXEoUmBbEISSXZkNTRHUgf46WahVQx7ACqmNuqmr+DZAmf9Nl/AbLERjVgu8/2IcD7\nPvt0NO/bkW7uvIxABBhgM8BuQJgB0ebPSJ+fTvMWav4ea0ATn8eDzfsRBgQZEG5YLOFGVFRNY/z4\nJ4zatZsZUVHVjJtvHmZs3brVqFGjkQHhhs0WY0yYMNF44omnjQoVahmVK9czJk9++4JtXrZsmdGg\nQWsjMrKq0avXTUZiYmKBz3fWrFlGrVqNjaioasbQoXcbaWlphbl8SimlVImQv9+lTx2ky9gzpnA2\nEFuIY+1HJsJO9tlu8fn9LeB2n8c+QqbFaYt0N3t0NdsF0qVc3eex3Uj3tK+Sfn/97tSpU2ZA6DCD\nuh8N2G8GfWMMOGDADDPYizXgDwMWGlDBDBJXGzDXfPxnA5aYj1UyYK8BywyIMeBlA/4wgoKGGg5H\ntAGdDNhpwFoDKhtBQY0M2GHAeiM0tIExffpX+bZ5//79Rnj4ZQZ8a8ABw+F4wOjU6doCne/atWsN\np7OyAf8zIMEICbnJGDLkrqK6nEoppZTfUAJBYUHGFCYg09JcZt4GIAPULlY4UqTyEFLB4KvURsRl\n2YcffogkTg0k2XsdUBE4DryN1AvdDPQCDpu/9wSGIsMyDwN9gFuQoZ/dgAeQ+L0mcBXwOPJdoRbZ\n2RNxuSx4l9NrBzxLdvblSPFKa9LTn+Krr37It81Lly4FeiAL59TE5ZrM2rW/kJGRccHzXbhwIdnZ\nI8zn1yYzczJz5+b/WkoppZTyKsiUNPWQOUzq+OxvAP0v4nUcSEA4Dek+BjiGdBsfRbqGj5vbDyHF\nKR41kQFuhzi76tmz3fOcWCSKsSNzqZzM24hJkyb9eT8uLo64uLiLOIWyp3379kgNThxSg4P5O8il\nqoVMNbMPmYfQgry1+5DlraPM3/cC15jP24d3JAHmceuZ9xORKXD2AVf6PO6di9Bq3UelSlH5tjky\nMhJJJnsmyz6I1WolKCjogucbGRmJw7GJ3FzPln2Eh+f/WkoppVRpER8fT3x8fIm2oSADGDcjXbi/\nIX+pQSKFJRfxGp8CScAjPttfMbe9jBSYRHN2oUkHvIUmDczXXI1UL69BqiR8C02aA/chYw1vRAtN\n2L17Nw0btkQuWxcks9cSWeIuFBgFrEBqf3KQJO5vwHLz9/uB9chE1aORGHwe8jEYC+zHZpuPw9GV\nrKzWhIR8QsWKDg4ePGXufwrp4U/BZrsfqzWNsLA5bNiwgjp16pyzzS6XiyuvvI5t25ykp7cnNPRz\nnnvuQR5//OELnm9qaiotW3bm6NHmZGXVx+n8hClT3mTw4Fsu5fIppZRSJaYkCk0K8mJrkADtUnUB\nliLBpScqm2Ae92skw5cADEaiCJClMu7CG6ksNLe3BaYi86nMwzu9TTCShWyNBJpDzGP6CrigcPny\n5XTp0hOojGTxHJw9lBMk62cgQeIZn+2heFdCOYUkdKF+/QbExtbCarVx5ZWdGTVqFHPmzCExMYlr\nr72GrKws+vS5E5frduAybLa9tG27k379rsXhsHPHHXdQo0aN87Y7KyuLqVOncujQYbp0uYqePXue\nd39fKSkpTJ06leTkU/Tq1ZPOncvaaoxKKaVU6Q0K70QmpluId/ZjkBRSWRJwQeHGjRtp3boLUsw9\nDkm6jkISr3uQmPtlpK5nBvAY0i1cBwkI45FY/lpatLiCXbsOYLE4aNQolvj4uURFnbtr9tVX/83E\niRMxDGjRoh3z539D5cqVi/NUlVJKqXKltAaFLyGB4W683ccgE96VJQEXFL7zzjuMHfs3JNPneas7\nIwUjQUivvm/NUEOke7kG8BmQBlTCYrkcuz0Rl+tXwE5Q0L0MHepgypR3833tnJwcMjMzCQ8PL/Lz\nUkoppcq7kggKC1JocgtQF1kaQ5Uh8+bNQ1Yo8dTyZCH1PAORGXzeRgLGaKQg/DgS++9Gsoj7gapY\nrZ1xuTyBJGRn3866dU+f97XtdrsGhEoppVQZUpApabYgq42oMqZx48bmvbbI0Mx2yBDOjubP1kAr\n4GHzvgXpVo5Avgs0xem8mkqVEgkOPo5UFhs4HHNo1uxyv56LUkoppYpXQTKFMchqI2vxjim82Clp\nVAlISjqJzEv4DBLspQMbgOlIZnAdsvRdFaQYfDoyP+EXwIuAneuuO87UqbPp1WsQ27c3wWIJoXJl\nePPNn/1/QkoppZQqNgUJCp89x7bAGpxXRs2bFw88h0wPAzIH+QBkqpnayKiAF/EOD81AppC5CkkQ\n16FOnUhiYmJYufJnNm7cSE5ODq1atSI4OBillFJKlR8FCQrj8/zeFbiNgs9TqEqIw2FBViLx2IPE\n8ynIWMJIZCafq5Gu4RlIYvhrZHnrND7+2M6cOd9SsWIl+vfvx7hx4/4MCBMSEvjiiy8xDINOnTqy\nfPkK1q5dS2pqKjVqVGf06NF071409UgZGRl88sknHDt2nK5du/DHH3+QkLCf2NhaHD8uS10PHXob\ndevWLZLXU0oppQJNQata2iCB4GBkuYpvkTWKy5KAqz6Ojq5OSsppZC7vaKTa2ImMJ+yNBISJSHCY\niRSX5yBzgB8EfkDGI64H7gESqFZtI1u3ruXo0aN07BhHRsZg3O6TuN2zgJFIcDkdcGK3Z/PWW/9i\nzJjRFEZmZibt28exZ09lMjNbYrH8B5stGpdrMPApFksNbLZ2OJ1fsWrVYpo0aVKo11NKKaVKWmmb\nkuZyJBC8FVncdgYwHokoyqKACwqt1hAMw4YEfE8ii748h3QNO5CAsCYwHCk+eRyZG9wzXPQupJs5\nHumCHorNNpSXXmrDmjW/8c03jTGMJ5HvCl3wziX+L2R+w1mEhYVy5kxSoc5j+vTpjB79IWfO/A/5\nyP4OdEJWMjyBdIMfw2L5gBtv3MjMmdMK9XpKKaVUSSuJoPB81cfbkQxhL2Riu7eQNJAqIwwjC5lG\nxoasc3wCCQId5h4VkTWPDeA6JFPo2/16OXAEyS7uASA3tz7JySmcPJmKYdQCViFLT9fzeZ5nBiMr\nGRmnKWwwnpKSQm5uHbz/Nmojcyi6gcvM9p3BMOqSlJRSqNdSSimlAtX5xhQOQjKFS5H1hWfg54hV\nFU6HDh1Ys2Y7ktxdhSxKkwl8gsT6byOB1dVI93AE8CjwATK34StIwXl94A3gOMHBX3HDDbOoUqUK\nixaNwzCqAKlIlrEe0v38PBKEVuKaa9p4vu1csh49emCxPI10Z7dEVkFshQSsk5HJtk8QGjqJ224b\nV6jXUkoppQJVQf5ahyMlq7chZaqfAd8BPxZju4pDwHUfSzBWHZmM2gnsRbJ/TiTpazP3dCPfD3J8\nnusxDJAAACAASURBVG1FprBZjYwrPAg05Y03XuChhx5k7NhHef/9JHJzpyKZxitxOHaRm5uL252N\n1RpCz57dmT79k3yXw7sYixcv5u67HyYx8TitWrXm6NFjHD16mAoVLiM19SRWq52HHrqPiROfKHQQ\nqpRSSpW00rqiyRngv+atAnAz8BRlLygMUA2RIBAkkxeCrG5yFCkIsSHjBaORFUzSke7lz5AJr9ua\nz61JVFQHGjVqCMDWrXvIzR2OfF4twDO0aTOZVasWFstZdO/enT17NhXLsZVSSilVsBVNfJ0EPgR6\nFENbVBGTDN1aYDGSGXwNCQIPI2P+HgAmAUlIFrExEGbucx9wDJhrHm072dkb/1wlpX375oSE/Bdw\nATmEhHxO+/Yt/HNiSimllCpygdTPFqDdx8FIkJeJjMNrg0xNA1AVWe/YgQSJbuAKpKs412dbOHa7\ni48+epfhw+8EZN7A66+/mTVr1gMWWrVqyk8/zSIsLMxv56eUUkqVV6VtSpryJgCDQivSVZyDdBUn\nAO8gPf8RyJQzi4A7kWlk9iNT02QhxSkrkICyPw0bHmHnzt/OOr5hGCQk/H979x0eZZX+f/w9KRMy\nSQihBqQ3kaZIEUUwFAs2dBXUVQRcdVlxLWt3lWW/P8uqi2WXFRWxoKtYEF0QURSiFKVIkw4RkCIt\nlAAJqfP74z5jBgwYIMkkk8/runLNM8+U55yZKHfuc+5zNuD3+2nSpInm8omIiJSQ8rYkjVR4fuBi\nLMgbBcwG/oBNDY3G5gwexKaIeoDG2LKUmUB7rMYoFriHfftyfvXuHo+HJk2a0LRpUwWEIiIiFdyx\ngsIDwP6j/GSUftOkZLwP9MW2tfsvtn1dIGM6C/sVmO3u52G7l4wEPgMCmcFUzjmnc1k1WEREREKg\nOOmdx7DKhLfd/euxdU4eLa1GlZJKOHzswYZ/dwOTsGzhcmxx6WpYEYoHywaeiRWWNAY+whaz3kxk\nZB2qV9/EwoWzqF+/Pn6/n507dxIdHU1SUlKx25Kbm8v27dtJSEhg//79JCcnExX128Xvfr+fbdu2\nER8fT0JCwnH0XkREpOIqr8PHlwMvYtnBDGA0tm6hVAgebF7hH7BNarzY9tX7sYKTPCwo/BaL9ydi\nfwN8D2wkP38+O3duY9q0r8jIyODccy+kYcNW1KnTkBtvvJX8/N/e5Gb27NnUrt2QZs3OpFq1OjRt\n2o46dRozb968Y75u27ZttGvXlaZN21GjRl3uu++Rk94dRURERIpWnKDwIHADlnKKxCKHA6XZKClJ\nkVjQ9z02hPxvbK7geqCbe84urDr5H0AnbD5hPpCIVSjDH/4wjB49erNgQTLZ2TvIzd3KhAkrefHF\nl4559aysLC655Cr27h1LTs4OYCa5uZHs3v13LrroSnJzc4/62htuGMrq1SkcOrST3NwNjB79MRMn\nTjzhT0JERESOrjhB4e+BAdjY4nZ3/PvSbJSUlGhspL870Madu47CfYPvwIpOGmIZxR+xLe6WA72B\nFCyQbIvfP4IlSzwusIsEEsjMHMTMmQuO2YKffvqJ/Px4rOAFoDPQFmhMdraHrVu3HvW1CxcuIC/v\nNte2mhw8OID5878/vo9AREREiqU4QeF6bAi5pvvph61tIuVePhbHB7KEYIUk2cC1wJdAfWx3Ew/2\nVXfGMouLsKDQA8zE9jb+xr1+NeAnJmYGLVs2OmYLkpOTycvb5V6Du9ZK4BAFBfupVavWUV9bv34j\nbMkcgDx8vm9o0uTY1xMREZETU5yg8FSsZHW5u98eeOQ4rvEaFpn8EHRuBLZC8iL30zfosYeAtcAq\n4IKg8x3de6wFXgg6HwO8585/ByhqOIwHCwhbAl2xrOHtwO/cY6uwrGEj91h3LJN3PbAGW88wsE1e\nFcBHXNxAEhI606JFGg8+eO8xr56YmMioUc8TG9udmJg+wGl4vbXw+Ybw6qsv4fP5jvrat956kWrV\n/krVqhcSH38GnTvHMGTIkBP9IEREROQYilPV8g1wH/AS0MG9ZhmF45G/pTs2B3Ec0M6d+xtW6fDs\nEc9tDbyDpatOwVJZLbA1VOZh0cw8YArwL2AqcBsWxdyGLbJ3JZYGO1IlrD6ugg0hn40VkuRhVcde\n7GuMAtKwryFQQ5SOzTlMBja55/0FmzHwPh7PP3noodvp1asX3bt3x+v1Fqsta9euZcWKFeTm5hId\nHU3btm1p1qzZb75u165dzJ07l4SEBLp160ZkZOTxfAQiIiIVUnmtPvYBc4Pu+7ENb4trJrCniPNF\ndbQf8K57/w3AOmyLjbpYyipQrjoOuMIdX07hvm0TsMlwAlgQmInF1lFY4UgPrBL5ABb0JWCJ3zew\nr7YK9vFvAxpgQ9DPY3H6m/j9N7FgwUJ69+79q4Bw5MgXqF27KTVrNuLhh0dQUFDAN998Q/PmHejU\nqQdjxrxD69atefbZVzjzzG60adOVxYsXH7MHNWvW5JJLLqFHjx4KCEVEREpRcYLCnUDzoPtXAz+X\nwLX/DCwBxmLpK7CqiM1Bz9mMZQyPPL/FncfdbnLHecA+rHpCyMeCvFgs6FsAJGGLVmdgQeIj2DDx\nnVhW8StsKPkBLFuYjM0j/BL7aP/HF1/MYPbs2cEX4u2332H48NHs3PkR6emf88ILU3j44eFcfPFV\npKWNICNjPtOmVaNTp17Mnt2BjIzvWbHiNlJS+rJr165S/yRERCT8jB37Oqee2oWWLTszZszYUDen\nwitOUHg7VpLaClvA7m7gTyd53dHYCspnYAHmyJN8PylSNFALuBBLwtbHlqSZgQWKPuB+LK6+i8JY\n+v+w4ebRWJzdDOiCBZAZwA28/fbbBBs/fhKZmQ9jX2krMjMfY/z4CcBFv1w7J2cUWVm7yM8f7q55\nI35/u99cr1BERORI77wznjvueIw1a/7B2rVPc9ddT/LWW/8NdbMqtN/eUsImnfUG4rAgcn8JXHdH\n0PGr2HYbYBnABkGP1ccyhFvc8ZHnA69piAWsgTHS3UVddMSIEb8cp6SkkJKScoLNryg82Ne1Hhsa\n9gA/uds8rABlPzaEfBD7WhKxaaSJ7nVVgt5vLVawkkbNmucedqXq1avi8XyP39/RPWc9VasmkJ6+\n4YhrR7jr1gZyKSjYRGJiYon3XEREwtvYse+Rmfk40AuAzMwnefXVtxg48PrQNuwEpaamkpqaGtI2\nHCsovCfouKgKjSOLRI5HXQqHoK+ksDL5f1ihybNYKqkFNo/Qj6WoznL3B2KFJoHXDMIqj6/Gxj+L\nFBwUVg4FWBBWgBV4dwJewQq2wUbtOwP9gY+xpWpGAuPduQHYEPQ9WAH5ROBM4uJW0rHjnQwbdje1\naiUxdOitbNmyFb9/lntOAT5fFmPGTGHo0HtYvfpSDh3qSGzsOLp27cPcuSkcPHg1Pt83dO16Kmef\nfXZZfSAiIhImfL4qWHFkQDrx8bFHe3q5d2Sy6u9//3uZt+FYVS0jsGDsVCxy+J97/qVYYHZDMa/x\nLnAetsbhdqzyOAUbZ/Rj6ag/uscAHgZuwlJZdwKfu/MdsYlxsVj18R3ufAzwFlYZnY5VHm8ooh2V\nsPq4KjYEfBXwOrbm30zgaWw2wHnYx9wFGIoFf9nY7iZR7vhyLJvYAo9nHJdd1pvevc/noYeeITPz\ndqKj11KlykTy8tqRlTUFiCEi4j569FjFjBmTOXToEK+//jo//7yNHj2606dPHz755BMWLPieJk0a\nc+ONNxZrD2QREZFg8+fPJyXlYjIz7wA8+HwvMH36ZM4666xQN61EhKL6uDgXm4ltRxEYNk7AgrLu\npdWoUlIJg8JqwGPYtFCwpWX+gw0J18CGcatiMfR32NzDz7C/A253x7djS0k2JTp6PjExGzhw4CCQ\nisXpEBHRmoKCYcAwd52PqFLlNurVq43HE83pp7fhyScfoWXLlr9q4759+/jhhx+oXr06rVu3LuFP\nQEREwtmSJUt45ZU38Pv93HLLIDp06BDqJpWY8hoUrgZOx9JHYBHFEixyqEgqYVAYiX1dD2NDwmlA\nU2xuX5T7OYAlhV8GLqFwVH4XNnXzHKwI5XNsJaC/Y4UoywkUgHs8KURFRZKbOxWb2nk6NtIfSAyv\npmrV51m2bD4NGhROGV28eDG9el1CQUEDcnI20b//ZbzxxujAfwgiIiKVVnldp3AcNlw8AosI5lK4\nLqCUa5HYmoPPYwHhR9i64+uxyuRobA3yD4GNWKAXCJxXYcUmHbEh5FFAY+BMbCHrwdhU0I+oUmUZ\nHTuCz9cCr7crNh9xMbZs5JXAg2RnX8aHH354WOv69x/Cnj1Psm/fd2RlrWbChO/4+OOPS+ODEBER\nkd9QnKDwcWAINta4G4sGnijFNkmJ8WBB3h4gBxseBhs67kHhmuJXYwFiYMfBu7F5iIdXGNtzAJ7D\nEse9SEi4k08//YDZs6cxd+5kbr31aqKjizdH8Kef1mLL1QDEk53dmzVr1hxnH0VERKQkFCcoBEv7\nfICVqKZjS8BIuefHtrTrg9XjTHDnt2JFJ3nASmxNwsBuJgux9cpvxOYUfovVGP0RyzZuwSqYl+Hx\nZDJt2of07NmTiIgI2rZty7333kuVKh9hdT9XARPxeJ4kJmYS/fv3P6x1LVu2w+N5y91LJyZmCu3a\ntUNERETKXnHGqv+MTQzbga1PElDR/vWuhHMKvdjyko9j2b9ILEu4yx3nYdm/VtgQ8Vgso3gIW9h6\nOx6PF48njoSEKtx445W8/fYk9u8/QM2aibz11mj69Onzq+uuXLmSv/71CZYsWYrHE8UZZ7TliSf+\n+qtCkzVr1nDeeX05cCCS3NydDBv2J0aOVBJaRESkvBaapGFrlqT/1hPLuUoYFAaKSXzY3MKzsLUJ\nM7AMYAIWAHqBFViQ2AhIwOv1M3DgFezbt5fmzVvQt+9F9OjRo8TbmJ2dTVpaGtWrVyc5ObnE319E\nRKQiKq9B4QzgAiyqqMgqYVAY544ux/YwHotlBnOwoeFzsaHhgdh6htcC8UAeUVEJ5OXFA22BM4iN\nfZNRox7jppsGH3c7li1bxrZt22jfvj21a9c+yV6JiIiEv/IaFL6G7Vv2KRZNgE1AO5kdTUKhEgaF\n0dga46+7M5OwNb/rAbODnlkDW7j6Z6xCOQfbzeRHbElKD7CExMSL2Lv3Z4rL7/czbNg9vPHGe3i9\nLcnPX87kyR9w3nnnnWTPREREwlsogsLilIn+5H687idQ0irlXjTQDHgPGzI+BfvqlmNTRGtjNUQH\ngX9iU0bHAw9iQ8lNKPx9bEJm5r7junpqairjxk0mK2sFWVmJwOdcddUN7Nq16WQ7JiIiIiWsOEHh\niNJuhJQWD/AMtph0PSzrF4MVmbTE1h9fjm1n1xNYiu0suBVbgeggNvTcBq/3Ifr0ufS4rp6Wlobf\n3w1b7xDgAnbv/pmcnBy8Xu9J9k1ERERKUnGWpJlRxM/00myUlJQcbEeSGdhSMgMoXF4mD9vizo9V\nKM8B1mBbVN+Dx5OL15tNZOTviYpqT0TENNasSWPKlCnFvnr79u2BL4HN7sw4GjRoqYBQRESkHCrO\nWHWnoOMq2OJzecB9pdKi0lMJ5xTGYPsdb8aGhXsADbCh5FRsh5It2FAyWOYwH9ueLhfIIz4+kdzc\nVmRnvwRsJDLyBoYMuZprrrmGHj16MH/+fKZMmUKrVq0YOHDgr9rwzDPP88gjw4mISCImJpfRo0fi\n8/lo27YtzZo1K9X+i4iIVFTltdCkKPOBziXZkDJQCYNCD7YcTRVsWckF2DzDqtiQcQaWKayLrThU\n4B4fgs0x/Bn7m2A3VoByMbaP8j+JiKhKUlIM6en7sF+F5Zx2WiOWL5932N7FmzZtolOn7hw8WJvs\n7O3k5x8kPv4s8vLmMWbMv7j++uvK4JMQERGpWMrr3sfVg35qAhdhUYWUe1FALLAOywzOwtYlvBdo\njQV5Mdh+xucBtYAs4H7s97AetrNJPeB6LIDcDgyioCCL9PR0974zgLWsXLmJ0aNHH9aCYcPuJz19\nMAcPvkZeXi5+/yr27/+UrKxUbr55KJmZmaX5AYiIiEgxFScoXAh8736+xdYu+UNpNkpOXlZWFlZQ\n0g5IcmfPwILAdUB3bCFrD7b0TF8s7vcC37jn52FfeQFWdHIz8BXw/4BM99rA7IJEoCPLly8/rB1p\naRvJz+8FbATaY39XALQhIiKBnTt3llifRURE5MQdKygM7G/cGFubpAnQAjgfSzlJORYTE4PND5wP\nLHNn38MCvHbYVtZp7jkdgZeBTVggOARIwYLIzdguJ14sQTwP+BDbDSUSeNu990pgJhdccMFh7Tjn\nnE7ExLyMzVf8HljkHplITIyfevXqlWCvRURE5EQda6x6EdDBHU/ACkwqsko1pzAzM5O4uCQKv+IY\nLOArcLdeLKg75B6PdLe5FO56Eu1+/EGvrQIU4PHk0b37ucyaNY+Cggggi8GDb+T118cc1o4DBw5w\nySUDmDv3W3JzD+HxROD1JhAbG8Vnn31Ely5dSukTEBERqbjK6+LVAE1LtRVS4rZu3YplASOB77CM\nnw8YRGGByQBgHDANuAkbCv4KyxD+DvgvsIjY2ARWrZpPZmYme/fupVatWtSoUYNq1aqRk5PD0qVL\nadasGUlJSUc2g/j4eFJTP2XXrl1ERUXh8/nYuXMnycnJREUV99dPRERESpv+VQ5T+fn57igS6IVl\n+lpgAWGWe2yie7wWhVvcxQKrgH8DG4AC8vJiWbRoEf369fvVdbxeL506dfrV+WAej4datWr9cr9+\n/fon2i0REREpJceaU9ge2O9+2gUd78ciCynHLO3sBd4F9gBfYwUmgd1NBmGZwwJsSZlcd9+DFZHE\nAmOAKeTmHuKbb7458hKABZ/33/8odeu2pGnTM3jvvfdLtV8iIiJSOo6VKYw8xmNSzn333XdYYBfI\n7p0JdMWWnpmEFZ38CfgPFiRuxzKIrbAM4VpgNLAeqE1ERNF/Pzz66P/jP/+ZQWbmBGAnN900kFq1\natKrV69S6pmIiIiUhuIsSSMV0KpVq4AD2N7GADuBH7Bg7xR37g1sEesnseLyVtj+x7WBN4HZ2FBy\nBgkJCUVe5913J5KZ+TyWTO5FZubdvP/+J6XQIxERESlNZREUvoaloX4IOlcdq25YA3yBLZgX8BAW\nuawCgtc36UhhVPNC0PkYLO21FquoaFSyza+Y4uPjsaHgs7Dt7Vq5+5Oxj8mPVRZHYR9dAbAVmIJl\nBy9z71QV6ElCQgLTp09n6tSp7N+//5frxMXFudeZyMgtJCbGl2rfREREpOSVRalzdyxlNQ5LJwE8\nDexytw9gqys/iG2z8Q42ye0U4EusOsKPLZB3u7udAvwLmArcBrR1t9cAVwLXFtGOSrUkzfz58+nS\npSsW9OVgS8kAxGPZwQIsI5iBLUwdDdzqbj8B7gZuweL59jRtWocdO6KJiEjA59vEd99Np1GjRkyd\nOpXf/e5GsrKGERW1k4SEiSxdOlfFJCIiIiehvG5zd7JmYpUOwS7Hxidxt1e4435YZUQuNrFtHZbq\nqoutljzPPW9c0GuC32sC0LtEW19BNWrUCAv8AOKw4DAO+AvwKPZ7lo4lZKdiH/krWJy+AbgLm2vY\nGNjPjz+24MCBBWRkpLJt20Bat+5KYmIyd9/9KHFxccTEvECTJrNp27Y9bdp0pkWLM5k9e3bZdRj4\n6quvaNr0dBIT69Kv3+/Zt29fmV5fRESkIgvVnMI6WAoKd1vHHdfDFtQL2IxlDI88v4XCiXGnYFtx\ngK27sg8bnq7Uxo8fj2X9WgALsNg8CXjJnc929/thxScNgJFYBXI1IBlL3Aa2pruEwj9YLiAzM5uM\njI9ZtepKdu3yk509n3Xr9jBrVmMyMuazbt0jXHjhFWzcuLFM+rt69Wouv/xa1q9/goyMeUydWoX+\n/QeXybVFRETCQXkoNPG7HylBDzzwAJYZfBabT3gGliFsiiVj47EAcDWwAitE6Q/c4I5/wuYePocF\ngy9iswDysIxiXffah7EgMwO/fxt+/yigPvA7PJ7eR13KpqRNnz4dv/8KLHhtQE7OKKZPn0JlmjIg\nIiJyMkK1ePV2LBW1DYsudrjzW7CUVUB9LEO4xR0feT7wmoZYtUMUkAjsLuqiI0aM+OU4JSWFlJSU\nk+pEeWbBUB62v3HAenduHYW7neRjO5m8hI3Qf4plF7sCHwOvYx/3Yuyr8mLL2yRgH/U+bBi6JhY8\nBvZKLgA2kJiYWGT7duzYwR//+BeWLFlOmzan8sorz1G3bt0T7m/VqlWJiFiP/X3hATYQG5sQmJMh\nIiJSrqWmppKamhrSNpTVv5iNscXxggtN0oGnsAKTahxeaNKFwkKT5ti/9HOBO7B5hZ9yeKFJO2zR\nvWuxuYaVvtDk3HPPdXP64oEh2HDxe+62LhaX52OLWS/FCkumY/H5Kiz4Wwu0ce8RixWsXI/HsxSP\nZzkFBTfj8UwgIsJDQcHVREb+h4iIeHJyBuPzzad16yzmzJlGdHT0YW3Lzc2lbduz+PHHXuTlXUtU\n1Ec0aPA/Vq78npiYmBPq76FDh+jU6Tx+/DGZQ4faExv7JiNHPsrQobec0PuJiIiEUigKTcriYu9i\nk9ZqYpHIcKy89X0sw7cB24R3r3v+w9hGvHnAncDn7nxHbGG9WKz6+A53PgZ4C+iABZrXuvc8UqUK\nCps3b05a2gZgGDa8uwz7uP+Lfe2dgEXACGwB6/rAIawi+X/uXfxAFRISYqhRoxYNG9anS5ezaNKk\nMbGxsfz443patmzBnj17SE/fTZ8+vcnKymLWrNmccko9Bg8eXGSQt2zZMs4++0oOHFjj2uInIaE9\nM2a8QceOHU+4z5mZmbz++uvs2LGTnj3DOxMsIiLhLVyDwvKiUgWFDRs2ZNOmrVjNzYdYgXYq9pVn\nAf+HBYQHgdOwpOyXWEz9KXA2lsh9D9hERMTVeL1TOP/8c9m9O4uWLRtx8cW9GDNmPAUFfv7yl5vp\n27dvsdq2bt062rfvQVbWeiymz8Xna8HcuZNp27ZtiX0GIiIiFZWCwtJVqYLCW265hVdfHYsNA0dh\nydce2LKQB7AlaG4H/u0e34MNEx/AArUcbF7hO1jyti02vJwP3EFk5Jfk578BPAP4iI29nw8/HMvF\nF1/8m23z+/1cckl/UlMzyMr6HbGxk+jaNYIvv/zkqNvpiYiIVCahCApDVWgipWzhwoXY8G81rPr4\nPOBvWMXxK9jQ8VNYQch0LDO4BxgEfADMxyqW/djcwgjgW2wjmknk59fAtsZrBFxEVlYEzzzzcrGC\nQo/HwyefvMsLL4xi4cIFnHFGT+666w4FhCIiIiGkoDBMLV++HMsSglUEDweuB2YBj2O7C34BfINN\n78zHdjBZh2UJL8SWp1mKLQN5kTt/KfBHbEOaH7EC8uMXHR3NvffefUKvFRERkZKnoDBMRUdHk519\nABsO3o0FhrWxzGAnYAZWWDIQKz5JxIq4U7GK5JXAGGz5mT14vc9SUFCVvLzngd+7q8S610YQG/sA\n9903tox6JyIiIiVN43VhqlatWtgcwS7YHMGa7pEId/wUFig+R+FOhKvc8XLgZaAltj5hHaKiYmjY\nsD6HLyPZhOTkn+jT5xMmTHitWEPHIiIiUj4pKAxTe/fuxRLBT2NrDf4ZW8j6NWynktnA37HAsZp7\n1XQsY1iAbYFXDatePoXs7JrEx0fg890D/ADMwut9jCuu6EVe3h5efXUsa9euLbsOioiISIlS9XGY\nqlOnDjt2HALuA16gcLvpHGxJmsuxOYUPYeuB78aGmpOxDGI2MBT4KzY38VYgjejor8jNBYjD44nC\n70/HhqA9REa+xaJFs2nXrh3Bli1bxrZt22jfvj21a9cu5Z6LiIhUfFqSpnRVqqCwfv36bNmSjg0d\nPwzc7x4ZiW0G8xC20cy12KLWa7Ft78CCwNZYgUkGVo3cxz12DTb38BEsuGyKVSS3BUbSo8dXfP31\nFMCWnhk27B7eeOM9vN6W5OcvZ/LkDzjvvPNKrd8iIiLhIBRBoYaPw9TBg5nYcjL1sUWpL8KGixsA\nVYCd2JIyfqyoJAHbRCYSqIFtd3czNgTdJOidW2JZRLBCk1rYUjUAzdi9e98vz0xNTWXcuMlkZa1g\n374ZHDjwFldddUPJd1ZEREROmoLCMJWcXAcL6H7CqolvB+Zg295twiqQ/4QFdTHY0HGgGP0AFjSe\nhg0334btHPgN8G88nl3AFmx7vM1Y1nA4cB+DBl39SxvS0tLw+7th8xQBLmD37p/JyckplT6LiIjI\niVNQGKZycnKxtQfbYLuOXApMxIaDAa7CFrJejs0tBFuI+n7gCmzHk5uwIeJvgbbExvZn1KgnaN16\nMdACK2IZgG09/Q+GDOnJPffc9Usb2rdvj2UpN7sz42jQoCVerxcREREpX7ROYZjy+wuwmD94HqXf\nnasDdAA+cuc7YBnBwJqF/YCqwCVERm4mKmoOnTt3YMCAAdxyy83k5+dx550/YPMLI7FFsWOAyMAc\nCAC6dOnCiBH38MgjrfF6axIbW8DkyZNKs9siIiJyglRoEqY8Hi+WKYzFMn49sTUJl2HZv0hsd5M2\nWHZwIfA1NmzcEMsiZmNFJ22AFUAkiYlVOOWUZFasuJrC4pXRwHC6dOnA3LmBrGOhPXv2sGvXLho1\naqQsoYiISDFo72MpQYGikTisOng90Bc4H/gnFvB1xQLHRsBZ7nU1sN/BQ+7+LKwSeSfQnn37qrFv\n31qsgCXgFCCetm1bFtmSpKQkkpKSSqxnIiIiUvKUKQxT9hdGHNAcKyj5o3tkPhYYggWEm7DFrB/E\n9kD+JzasnA7UwwpKAs4HYvB45uL3V8PmEkYB15CUlEVa2nIFfyIiIiVA6xSWrkoVFCYlJbF3bya2\n/EwbYCoWJA4CJmGZwvrYsPCVWGVxoO4oF9sneSfwLrbQ9UIsKPQRHZ1Bbu5grIgkm8jI7WzY7bHe\nrQAAFx5JREFUsJL69YOzhyIiInKitE6hlJiDBwuwBad9wFJs6ZlErJI4B9vK7idsK7uZWDYxDStG\n+Qu2BM0krIgkETgH2Efnzk35/POPqVp1PLGx6cTH72XSpPcVEIqIiFRwCgrDVG5uJrAO256uI7Zl\n3RJ37mFsvqEPGzbOx+YQerDK5FrANqw45TagJkOHDmHLlp+YN+9revbsya5dm1mzZgHp6Vvp27fv\nr66fnZ3N5s2bybU98URERKSc0/BxmLK0cwyQhM0PjMG+7oeA6tieyHlYdfEhLHsYj2UQc93504DA\nQtXRXHPNVbzzzutERBz7b4lPP/2Ua665kYICL16vn//973169OhRCr0UEREJTxo+lhIUhf0uebHF\nozOwXU2eAR7AhonfBfZiO53EY+sUZgA/YwUq1YBobAu8C3n//amMHfvaMa+6Y8cOBgwYxMGDk8nK\n+pl9+8Zx2WX9OXjwYCn0UUREREqKgsKwlYdl/27EikY82FBwLrAPC/j6ueeeiQWRd2C/EtWB67B9\nknOwQhM/fv9eZs/+/phXXblyJdHRpwJnuzMX4PcnsWHDhhLrmYiIiJQ8BYVhLRL4HAsEAaZjw8lx\nwG5sizuwKuMCrJoYbI7h18B+LDCchWUcE2jZstExr9iwYUOys9cAW92ZdeTmbqNu3bol0SEREREp\nJaEOCjdgpbGLgHnuXHVsteU12Ka81YKe/xCwFlgFXBB0viPwg3vshVJtcYWSC+zAhoIvxIaNP8Ey\ng/nYgtXnAadiGcH7sAWtWwLfA1OwHVDSgU9ISIjkrrvuOOwKfr+fUaNG06FDCt269WXjxo0MH/4A\nsbEdqVr1Uny+bjz//D+pXr16WXRYRERETlCoC03WYwHd7qBzT2PVDU9jUUwSViLbGngH6IxtofEl\n0AKbHDcPmzA3D4tk/oUtzBeskhWaBHYzqQlsBN7DAsBawNVYlfF4bE6hH1vPMBrIxD7iVdhahlZ8\nEhmZwaJFC2jXrt1h1xk58gWGD3+VzMyRwE58vruZMWMycXFxrFu3jtNOO42WLYve6URERESKVlkL\nTY7s8OXAm+74TeAKd9wPq4zIxTKM67BUV12sEiKQaRwX9JpKLAJoBTTD9j8eBDTF1hychAWJvbCP\nLhYLCHPc8UJsODlQqNKdgoJY+vYdwAcffHjYVV588Q0yM1/GErfXk5l5F+PGjadNmzb069evRALC\n3bt3c/HF/UlMrEvz5h2YOXPmSb+niIiIHC7UQaEfy/gtAG5x5+oA293xdncfbM+1zUGv3YxlDI88\nv8Wdr+QKsCBvBRYYtsM+5lQsW9gAmOGedylWjBILfIatX3g5sBJ4GfgKv9/Pli0vM3jwn5kzZ84v\nV4mKCmQXAw7wGyvWHLfLL7+OL7+sRUbGfNLS/kbfvr9T4YqIiEgJC3VQ2A3oAPQFhgHdj3jc737k\nuHmxhOo+bBj5CWzuYAfg70AjLDt4DjAW290kA6ta3g+8hAWOFwOXYUPJncnKupVPP/3sl6sMH34n\nERHXA2OAx4FRzJo1l4KCghLpRXZ2Nt9+O4Pc3H9h2/JdgcdzAV9//XWJvL+IiIiYqBBf/2d3uxOY\nCHTBsoPJ2JYadbFKCbAMYIOg19bHMoRb3HHw+S1FXWzEiBG/HKekpJCSknKSzS/PPBTOFYzCpm8G\nrHW3UUBb95gPOIhlDKtgAWVr9x5pBBa6jo5eT1JS+1/e6fTTTyc62kN29kz3HrNYs+ZyVq1aRevW\nrU+6F9HR0URFecnJ2QQ0wTKbG0hMTDzp9xYRESkvUlNTSU1NDWkbQllo4sPWTNmPpbK+wFJYfbBy\n16ewApNqHF5o0oXCQpPmWNQyF1tkbx7wKSo0cRNUA7uaRLvjIVg2MDAvMAcbtX8XC/oSgPOxjzkR\nuBmYD3wHtCUmpgm1ai1g6dLvSEpKAmDJkiWce25/DhxY7a5RQFxcS779duKvilJO1LPP/otHH32O\nrKyBxMYu4LTTDjJnzjS8Xm+JvL+IiEh5E4pCk1AGhU2w7CBYyuq/wJPYkjTvAw2xdNUArEQWbNPe\nm7AI5k5sET6wCuY3sElxU7AA8UiVMCiMwWLmfHccgQWCBdhUzO3YMLMfGx7Oxb6KSGzx6XTsK0jm\n0ktb0atXTwYPHvxLQAiQl5fHGWd0Y+3aM8nJuRqv931OPXUpCxfOJCqq5BLR06ZNY+bMWdSrV5ch\nQ4YQExNTYu8tIiJS3lS2oLCsVbKgMNId1cQKQbxY0OfHsoGfuXN/xOLx2sC/3fnA/L0soCGxsV5G\njx7IoEGDfnn/OXPm8NhjL3DoUDY33ngFX389j8WLV9ChQxueffZxqlULXl5SREREjoeCwtJVyYLC\nwFfrA/6MZf5GYhXFmVg20I8Vdz+FFZo8AczGilGqAK8AS/F4RrJmzWKaN28OwPz580lJuZjMzMeB\nqvh8D/Lyy09www2/L7P+iYiIhLNQBIWhLjSRUhODDROfBfzDnUvBMoLRwP3AM9hI/Znu8a3AaCxY\njMfWObyKmJiVpKam/hIUvvjia2Rm3g/cCkBmZiJPP62gUEREpCIL9ZI0EhIe4FUsaHwSmzeIu/8G\n8H8E/3Fy6NAhPvnk0zJtoYiIiJQtBYVhKxurIJ5L4Z7HlwFVgZ7YMjQ3Y8vNdAIewbKEg91rDmJb\n3f0N+JavvprLggULALjttpvw+Z7GFrYej8/3Jx544E9l1jMREREpeZpTGKY8Hp87yseWdwxUI1+B\n7QT4EzALuBcr3v4KW/YxDgsIc7Ft8eKAzcTENGXs2GFcf/31gBWaPP74Cxw6lMPQodfTv//VZdY3\nERGRcKc5hVKCAtvcgVUbVwG+x4aKM7B5g//FFq+uCSRha35nuNfGY/MPtwNNyM5eTevWrVmxYgUT\nJnxETIyXMWOeo169emXYJxERESktCgrDkGVEA8PHA4C3sKBvCJYRLMACwVrY0PIdWAB5KxY0zsHW\nMZyOBY8XkpAQRVZWFueeewHZ2YOJiMjgqae6sHjxtzRo0AARERGp2DR8HIb8fj8REQnYpi/52BzC\njdiugfnYHshbsOHh2kB/LDj8AuiKzT1cBLztXvsUvXt/zYEDecydOxAYCEBk5AMMHZrLqFHPlmHv\nREREwl8oho9VaBKG7BcpH9s0ZieWEE52j0ZicwyvwOYOfuAeP4RVJG9zr4vDNpLZS0zMO9x00w3s\n25fhHjP5+U3YtWtfWXRJRERESpmCwrDlwYaDG2HrEt6LFZKMx/Yzfsad74LNLbwcCxBPA94ENhMb\nOxSvtyGDB/fkuuuuY8CAS/H5HgTWAgvw+Z7h2msvK+uOiYiISCnQ8HGY8ni8WLWxB9uyLh04ACQA\nY4BzsYxhW+Bb97y3sF1NtpCQUIXZs7+iXr161KhRA4D8/Hzuv/8R3njjv0RHexk+/D5uu+2PZd01\nERGRsKdt7kpXJQsKo7FEsAd4EeiMBYNTgQuBz4HG2JZ3s9yrlgH9gDw8nv28/fYofv977VIiIiJS\n1jSnUErEgQMHsKrhGtgQ8YdYIJiMVR2PA24AumN7IW/EgsPATiY34Pf3YubMmWXfeBEREQkJLUkT\nhpYtW4YtO7MbWIJVG28DWrvzEUAe8AIWDLZw52OBK7FdTB4iOblqmbddREREQkNBYRiqWbMmNp+w\nKrZe4XKsgKQFNq/wJ+AdLAj0Y0FirHvOQ8AXxMaOo1+/aWXfeBEREQkJBYVhqHnz5tgSM1HY0jN+\nbOh4OeBz5ya7Zx/CAsj92NqEZwFeoqMjiImJKeOWi4iISKhoTmHYigWuBtKwJWRqYsHhpcAKbNHq\n/VhAGA3c4163GdjF/v3/j/79h5R1o0VERCREFBSGoYKCAizYuwErHIkCrsd2K5kE/AdY7+4PB1oB\nHbB5hTaP0O8fwLp1K8q66SIiIhIiGj4OQ7t378YKSd4BemG7m0wAugENgcVYsJgNPAJUARYCXiAD\nqIrH8z7Nm7cu+8aLiIhISCgoDEO2tlEOthZhPSzYa4ntb9wNG1KOw4aTf8T2N34WCxjrU7VqE7ze\nvXzwwdQQtF5ERERCQUFhGNq4cSMWCO4AemCZwVZAXywIjMN2MvnMHf8B+A74hieffITzz+/Naaed\nhs/nC0XzRUREJAQUFIahU045BcjFhoizsHmE32LDxYuxIeJrsIAwH/gayKROnao8+OD9IWmziIiI\nhFY4bXN3EfA8EAm8Cjx1xOOVapu7U09tw5o1P2KVxTWButiSM1lAU2wx67bALqwKOYt16xbTrFmz\nELVYREREArTN3YmLBEZhgWFr4DpsJeZKa/Xq5TzwwJ1YwLcemIMFhAA/4vNBq1YZtGgRyU03XUZ6\n+kYFhCIiIpVYuGQKz8b2ZrvI3X/Q3f4j6DmVKlMoIiIiFZcyhSfuFGBT0P3N7pyIiIiIFEO4BIVK\nAYqIiIichHCpPt4CNAi63wDLFh5mxIgRvxynpKSQkpJS2u0SERER+U2pqamkpqaGtA3hMqcwClgN\n9Aa2AvOwYpOVQc/RnEIRERGpEEIxpzBcMoV5wO3YFh6RwFgODwhFRERE5BjCJVNYHMoUioiISIWg\n6mMRERERCQkFhSIiIiKioFBEREREFBSKiIiICAoKRURERAQFhSIiIiKCgkIRERERQUGhiIiIiKCg\nUERERERQUCgiIiIiKCgUERERERQUioiIiAgKCkVEREQEBYUiIiIigoJCEREREUFBoYiIiIigoFBE\nREREUFAoIiIiIigoFBEREREUFIqIiIgICgpFREREBAWFIiIiIkLogsIRwGZgkfvpG/TYQ8BaYBVw\nQdD5jsAP7rEXgs7HAO+5898BjUqr0SIiIiLhKlRBoR94Fujgfj5z51sD17jbi4AXAY97bDTwB6CF\n+7nInf8DkO7OPQc8VfrNrzhSU1ND3YSQUL8rF/W7clG/K5fK2u9QCOXwsaeIc/2Ad4FcYAOwDjgL\nqAskAPPc88YBV7jjy4E33fEEoHfpNLdiqqz/ManflYv6Xbmo35VLZe13KIQyKPwzsAQYC1Rz5+ph\nw8oBm4FTiji/xZ3H3W5yx3nAPqB66TRZREREJDyVZlA4DZsDeOTP5dhQcBPgDOBnYGQptkNERERE\nfkNRQ7hlrTEwCWgHPOjO/cPdTgX+BmwEZgCnufPXAT2AP7nnjMCKTKKwILNWEddZBzQr6caLiIiI\nlII0oHmoG1EW6gYd3w28445bA4sBL5ZJTKMwcJ2LzS/0AFMoLDS5Dcs8AlwLjC+1VouIiIhIiRoH\nLMXmFH4M1Al67GEsq7cKuDDofGBJmnXAv4LOxwDvU7gkTePSarSIiIiIiIiIiIiEUH9gOZAPnHnE\nYyW50PUgYI37uTHofBNs6HotNjQdHfTYv9z5Jdh6i+XNRdhnsxZ4IMRtOZbXgO3YdxZQHStWWgN8\nQWGFOoT+ey8pDbC5ssuBZcAd7ny4972Ke//FwArgSXc+3PsNEIkt2D/J3a8MfQZbYmwp1vfA0mKV\noe/VgA+Bldjv+lmEf79PpXBjikXYiiB3EP79BuvHctfmd1w7K0O/y1QroCX2j2dwUBiYfxiNDRmv\no3D+4Tygizs+cv7hi+74GgrnH1bH5i9Wcz9pQKJ77H1ggDseDQx1xxe79wb7D/27E+teqYnEPpPG\n2Ge0mMJCnfKmOxZUBweFTwP3u+MHKCw+CvX3XpKSsQp8gHhgNfYdVYa++9xtFPbfzrlUjn7/Bfgv\n8D93vzL0GWA9v14qrDL0/U3gJncc5a5dGfodEIEVfzYg/PvdGPgRC9zAgrdBhH+/Q+bIoPAhDs9+\nTQW6YkUsK4POXwu8FPScs9xxFLDTHV9HYbEK7vnXYl/QTgqX7unq3gPgZexLCVjF4XMjQ+1sCtsK\nVtn94FGeWx405vCgMPjzTHb3IfTfe2n6GOhD5eq7D5gPtCH8+10f+BLoSWGmMNz7HLAeqHHEuXDv\neyIWJBwp3Psd7AJgpjsO935Xx/6wT3JtmgScTwXrdygXrz5ZJbXQdY1jvFd1YC9QUMR71Qt6r8Br\n6p9wb0pecF+hsE8VRR1sSBl3G/iPKtTfe2lpjGVL51I5+h6B/ZW8ncIh9HDv93PAfUHXgPDvc4Af\nC4gXALe4c+He9ybYP8ivAwuBMUAc4d/vYNdiu5RB+Pd7N7bm8k/AVne9aVSwfpeXoPBoC11fFsI2\n+YvxnCPXeSzOa8pKeWrLyfJTdv0JxecWj23ReCew/4jHwrXvBdjQeX1szdGeRbQlnPp9KbADm2N1\ntPVhw63Pwbphf/T0BYZhU0aObE+49T0KG9160d0e5NejNeHY7wAv9m/4B0dpS7j1uxlwF/YHfj3s\n/+s3FNGWct3v8hIUno8tXn3kz6RjvGYLNk8hoD4WHW/h8Ixd4HzgNQ3dcWB+R3oR79XAnduNjc8H\nPqf67vzRrr+F8qOoPm0+ynPLo+1Yqh0snb7DHYf6ey9p0VhA+BY2fAyVp+9gf+l+ik2sDud+n4Pt\n5rQey5z0wr7zcO5zsJ/d7U5gIjZfKtz7vtn9zHf3P8SCw22Ed78D+gLfUzjEGe7fdydgjmtHHvAR\nNo2rsnzfZW4G9g9HQEkudF0dm/tRDZsPEDgGm6QZmDv4EkUXmnSl/BWaRGGfSWPsMyrPhSbw6zmF\nT1M43+JBfj05N1Tfe0nyYGt2PnfE+XDve82ga8UC3wC9Cf9+B5xH4R+8laHPPiDBHccBs7G5ZpWh\n799ghZJgO289TeXoN64tg4Luh3u/T8dWkYh17X0Ty4qHe7/L3JXYGHoWFnF/FvRYSS50PcSdX8vh\nv8jB5dzvcXg59yh3jSX8ermc8qAvNvF1HTaptbx6F5uDkYN910OwX/IvKbqMP9Tfe0k5FxtGXUzh\n8g0XEf59b4fNsVqMLVNynzsf7v0OOI/C6uPK0Ocm2He9GPtHM/D/osrQ99OxTOESLHOUSOXodxyw\ni8I/BqBy9Pt+CpekedNdozL0W0RERERERERERERERERERERERERERERERERERERERERERERERKQ8\nuAJb4/HUEFx7A7b2WHHPi4hUCOVlmzsRkeNxHTDZ3Za1o+0pGk77jYuIiIiUe/FYVq4hsDLofAqQ\nCnzgzr8d9NgGbJux77FdVAIZxhHAPUHPW0bh3qITgQXu3C1Bz1lP0RnBwPnG7vqvuNd+DlRxz2mO\n7W6w2LWliTv/DLaDwVJgQFB/vsb2xE7DtscaCMxzz2vqnlcL21d3nvs5p4i2iYiIiISd67E9PMH2\nlg1sMZkC7AXqYXuGzqEwQFqP7UMK8CdgjDv+G4cHhT9QGBQmudtYdz5wvzhBYS7Q3p1/z7UZbLup\nfu7Y6977Kmz7Kw9QG9gIJLv+7AHquOduwYJYgDso3DP7HaCbO24IrCiibSIiv0nDxyJS0VyHZQNx\nt8FDyPOwfbT9WDaucdBjH7nbhUecP5o73Xt8CzQAWhxHG9dj2TywjGBjLMNZD/jEnc/B9nTvhgV2\nfmAHlh3s7O7PB7a7567Dso5gGchAH/pg+7Avcu+dAPiOo60iIgBEhboBIiLHoTrQE2iLBU2R7vY+\n93h20HPzOfz/cdlFnM/j8D+OA8O8KUBvoCtwCJgR9FhxHNmO33qt54j7gfmJwe9TEHS/gMI+eICz\nsMBRROSEKVMoIhXJ1cA4LEvWBBsuXQ90P8H320Dh8POZFM7xq4oN3R4CWmHB4cnwAAeAzRQOH8dg\nw8czgWuw/x/XAnpgGc8jA8Wj+QIbTg444yTbKiKVlIJCEalIrsUKQIJNwIaQ/RSvAjj4eROw7OMy\nbM7hand+KpaJWwE8iQ0hF+d9izoOvj8QC+CWALOx+YITsaHmJcBXWNZzx2/0J/ixO4BO7vXLgVuL\n0VYRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERkaP7/4ubM74H\ni8gVAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1379c6e50>"
]
}
],
"prompt_number": 395
},
{
"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": 396,
"text": [
"<matplotlib.text.Text at 0x116dbd810>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+/QcpEilQOFxNkUihYrE6CoVSGjr0iipdx+vXr1ePHocqGq2uSKRA/fsPUnFx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\nwUg/XWzuPj93sOpDqaA9t3GBerd8Dp6xpgNL098VWfoeu7OWnx0OnwRL3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hFFt6nqY3+v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8FGQp87HtgePPW/Zbyr77s68jeAmvhOzIPrU+CeAEyJ4pAXmkHm7PLbLnktAxXA/ZRK20urXh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\nNVEH698Z0BxyLygYWk3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M9IHXv7UybWPh2gDTcPQAb7RVoJbRNFJ1YECFaQmGwKPkHtZ5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pYjwnioPx9RmKHBOZF0qi9GRAYDHQ7/1f59JFkT8zJ0mMeBHj6NEUwRDMrFZLqV+kKb0f5Y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\nx9yEGPGkLRUVFQwbNp40K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JW0PWyDFlCG/z7KGN+x+zkCaKmXI3Nr1Ew0tuk1QvxQtE7l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"text": [
"<matplotlib.figure.Figure at 0x13799b650>"
]
}
],
"prompt_number": 396
},
{
"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": 422,
"text": [
"<matplotlib.axes.AxesSubplot at 0x127662390>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QIkGUo9Vjhqm/fJiWVYhhQ/U7DbSd4qMcLSGEEEKIPpF0oOWpD7aKfpjyGoZp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\naxHM7uM17Bh/eBP93Zjf+nH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m9kov0mIXLLB5JXZ3/jOxB6DOlKqBUyU/KQdaQogGKQcf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\nUwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1275adb10>"
]
}
],
"prompt_number": 422
},
{
"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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//TZ8/eu3Lc/bzcDF3PP4EQBvAfAz577/GIDXAPgld86Lfs/DK4ROSsjHZn1C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\nD6d5TyBKTlkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x139a9e050>"
]
}
],
"prompt_number": 424
},
{
"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. |"
]
}
],
"metadata": {}
}
]
}
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