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@odubno
Last active August 29, 2015 14:07
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Oleh_Dubno_Animal Sleep vs Animal Weight
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legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
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.form-horizontal .control-group:after{clear:both}
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.form-horizontal .form-actions{padding-left:180px}
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.table th{font-weight:bold}
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.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
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.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
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.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
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.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
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.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
.table tbody tr.success>td{background-color:#dff0d8}
.table tbody tr.error>td{background-color:#f2dede}
.table tbody tr.warning>td{background-color:#fcf8e3}
.table tbody tr.info>td{background-color:#d9edf7}
.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
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.icon-music{background-position:-24px 0}
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.icon-envelope{background-position:-72px 0}
.icon-heart{background-position:-96px 0}
.icon-star{background-position:-120px 0}
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.icon-cog{background-position:-432px 0}
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.icon-home{background-position:0 -24px}
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.icon-upload{background-position:-144px -24px}
.icon-inbox{background-position:-168px -24px}
.icon-play-circle{background-position:-192px -24px}
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.icon-headphones{background-position:-336px -24px}
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.icon-plane{background-position:-168px -120px}
.icon-calendar{background-position:-192px -120px}
.icon-random{background-position:-216px -120px;width:16px}
.icon-comment{background-position:-240px -120px}
.icon-magnet{background-position:-264px -120px}
.icon-chevron-up{background-position:-288px -120px}
.icon-chevron-down{background-position:-313px -119px}
.icon-retweet{background-position:-336px -120px}
.icon-shopping-cart{background-position:-360px -120px}
.icon-folder-close{background-position:-384px -120px;width:16px}
.icon-folder-open{background-position:-408px -120px;width:16px}
.icon-resize-vertical{background-position:-432px -119px}
.icon-resize-horizontal{background-position:-456px -118px}
.icon-hdd{background-position:0 -144px}
.icon-bullhorn{background-position:-24px -144px}
.icon-bell{background-position:-48px -144px}
.icon-certificate{background-position:-72px -144px}
.icon-thumbs-up{background-position:-96px -144px}
.icon-thumbs-down{background-position:-120px -144px}
.icon-hand-right{background-position:-144px -144px}
.icon-hand-left{background-position:-168px -144px}
.icon-hand-up{background-position:-192px -144px}
.icon-hand-down{background-position:-216px -144px}
.icon-circle-arrow-right{background-position:-240px -144px}
.icon-circle-arrow-left{background-position:-264px -144px}
.icon-circle-arrow-up{background-position:-288px -144px}
.icon-circle-arrow-down{background-position:-312px -144px}
.icon-globe{background-position:-336px -144px}
.icon-wrench{background-position:-360px -144px}
.icon-tasks{background-position:-384px -144px}
.icon-filter{background-position:-408px -144px}
.icon-briefcase{background-position:-432px -144px}
.icon-fullscreen{background-position:-456px -144px}
.dropup,.dropdown{position:relative}
.dropdown-toggle{*margin-bottom:-3px}
.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
.dropdown .caret{margin-top:8px;margin-left:2px}
.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
.open{*z-index:1000}.open>.dropdown-menu{display:block}
.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
.pull-right>.dropdown-menu{right:0;left:auto}
.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
.dropdown-submenu{position:relative}
.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
.dropdown-submenu:hover>.dropdown-menu{display:block}
.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
.dropdown-submenu:hover>a:after{border-left-color:#fff}
.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
.btn:active,.btn.active{background-color:#ccc \9}
.btn:first-child{*margin-left:0}
.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
.btn-block+.btn-block{margin-top:5px}
input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
.btn-primary:active,.btn-primary.active{background-color:#039 \9}
.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
.btn-success:active,.btn-success.active{background-color:#408140 \9}
.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
.btn-info:active,.btn-info.active{background-color:#24748c \9}
.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
.btn-group+.btn-group{margin-left:5px}
.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-group>.btn+.btn{margin-left:-1px}
.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
.btn-group>.btn-mini{font-size:9.75px}
.btn-group>.btn-small{font-size:11.049999999999999px}
.btn-group>.btn-large{font-size:16.25px}
.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
.btn .caret{margin-top:8px;margin-left:0}
.btn-large .caret{margin-top:6px}
.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
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.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
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.btn-group-vertical>.btn-large:last-child{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.alert,.alert h4{color:#c09853}
.alert h4{margin:0}
.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847}
.alert-success h4{color:#468847}
.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48}
.alert-danger h4,.alert-error h4{color:#b94a48}
.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad}
.alert-info h4{color:#3a87ad}
.alert-block{padding-top:14px;padding-bottom:14px}
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.alert-block p+p{margin-top:5px}
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.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}
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.nav-tabs{border-bottom:1px solid #ddd}
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.nav-pills.nav-stacked>li>a{margin-bottom:3px}
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.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}
.nav-tabs .dropdown-toggle .caret{margin-top:8px}
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.tabbable:after{clear:both}
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.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
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.tabs-right>.nav-tabs>li>a{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
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.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #fff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #fff, #f2f2f2);background-image:-o-linear-gradient(top, #fff, #f2f2f2);background-image:linear-gradient(to bottom, #fff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065);*zoom:1}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0}
.navbar-inner:after{clear:both}
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.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}
.navbar-text{margin-bottom:0;line-height:36px;color:#777}
.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}
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.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0}
.navbar-form:after{clear:both}
.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px}
.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}
.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
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.navbar .nav>li{float:left}
.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}
.navbar .nav .dropdown-toggle .caret{margin-top:8px}
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.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}
.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}
.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}
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.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
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.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}
.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
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.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
.breadcrumb>.active{color:#999}
.pagination{margin:20px 0}
.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
.pagination ul>li{display:inline}
.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
.pagination-centered{text-align:center}
.pagination-right{text-align:right}
.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
.pager:after{clear:both}
.pager li{display:inline}
.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
.pager .next>a,.pager .next>span{float:right}
.pager .previous>a,.pager .previous>span{float:left}
.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
.modal.fade.in{top:10%}
.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
.modal-header h3{margin:0;line-height:30px}
.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
.modal-form{margin-bottom:0}
.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
.modal-footer:after{clear:both}
.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
.modal-footer .btn-group .btn+.btn{margin-left:-1px}
.modal-footer .btn-block+.btn-block{margin-left:0}
.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
.tooltip.top{margin-top:-3px;padding:5px 0}
.tooltip.right{margin-left:3px;padding:0 5px}
.tooltip.bottom{margin-top:3px;padding:5px 0}
.tooltip.left{margin-left:-3px;padding:0 5px}
.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
.popover.right{margin-left:10px}
.popover.bottom{margin-top:10px}
.popover.left{margin-left:-10px}
.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
.popover-content{padding:9px 14px}
.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
.popover .arrow{border-width:11px}
.popover .arrow:after{border-width:10px;content:""}
.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
.thumbnails:after{clear:both}
.row-fluid .thumbnails{margin-left:0}
.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
.thumbnail .caption{padding:9px;color:#555}
.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
.media,.media .media{margin-top:15px}
.media:first-child{margin-top:0}
.media-object{display:block}
.media-heading{margin:0 0 5px}
.media>.pull-left{margin-right:10px}
.media>.pull-right{margin-left:10px}
.media-list{margin-left:0;list-style:none}
.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
.label:empty,.badge:empty{display:none}
a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
.label-important,.badge-important{background-color:#b94a48}
.label-important[href],.badge-important[href]{background-color:#953b39}
.label-warning,.badge-warning{background-color:#f89406}
.label-warning[href],.badge-warning[href]{background-color:#c67605}
.label-success,.badge-success{background-color:#468847}
.label-success[href],.badge-success[href]{background-color:#356635}
.label-info,.badge-info{background-color:#3a87ad}
.label-info[href],.badge-info[href]{background-color:#2d6987}
.label-inverse,.badge-inverse{background-color:#333}
.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
.btn .label,.btn .badge{position:relative;top:-1px}
.btn-mini .label,.btn-mini .badge{top:0}
@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
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.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
.accordion-heading{border-bottom:0}
.accordion-heading .accordion-toggle{display:block;padding:8px 15px}
.accordion-toggle{cursor:pointer}
.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}
.carousel{position:relative;margin-bottom:20px;line-height:1}
.carousel-inner{overflow:hidden;width:100%;position:relative}
.carousel-inner>.item{display:none;position:relative;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}
.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}
.carousel-inner>.active{left:0}
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.carousel-inner>.active.left{left:-100%}
.carousel-inner>.active.right{left:100%}
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.carousel-indicators .active{background-color:#fff}
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.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
.carousel-caption h4{margin:0 0 5px}
.carousel-caption p{margin-bottom:0}
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.hero-unit li{line-height:30px}
.pull-right{float:right}
.pull-left{float:left}
.hide{display:none}
.show{display:block}
.invisible{visibility:hidden}
.affix{position:fixed}
@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
.visible-phone{display:none !important}
.visible-tablet{display:none !important}
.hidden-desktop{display:none !important}
.visible-desktop{display:inherit !important}
@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-tablet{display:inherit !important} .hidden-tablet{display:none !important}}@media (max-width:767px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-phone{display:inherit !important} .hidden-phone{display:none !important}}.visible-print{display:none !important}
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span.raw_input_prompt{}
input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;vertical-align:baseline;padding:0 .25em;margin:0 .25em}
input.raw_input:focus{box-shadow:none}
p.p-space{margin-bottom:10px}
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.rendered_html strong{font-weight:bold}
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.rendered_html ol ol ol ol{list-style:lower-roman;margin:0 2em}
.rendered_html ol ol ol ol ol{list-style:decimal;margin:0 2em}
.rendered_html *+ul{margin-top:1em}
.rendered_html *+ol{margin-top:1em}
.rendered_html hr{color:#000;background-color:#000}
.rendered_html pre{margin:1em 2em}
.rendered_html pre,.rendered_html code{border:0;background-color:#fff;color:#000;font-size:100%;padding:0}
.rendered_html blockquote{margin:1em 2em}
.rendered_html table{margin-left:auto;margin-right:auto;border:1px solid #000;border-collapse:collapse}
.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid #000;border-collapse:collapse;margin:1em 2em}
.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px}
.rendered_html th{font-weight:bold}
.rendered_html *+table{margin-top:1em}
.rendered_html p{text-align:justify}
.rendered_html *+p{margin-top:1em}
.rendered_html img{display:block;margin-left:auto;margin-right:auto}
.rendered_html *+img{margin-top:1em}
div.text_cell{padding:5px 5px 5px 0;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
@media (max-width:480px){div.text_cell>div.prompt{display:none}}div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:.5em .5em .5em .4em;color:#000}
a.anchor-link:link{text-decoration:none;padding:0 20px;visibility:hidden}
h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible}
div.cell.text_cell.rendered{padding:0}
.widget-area{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-area .widget-subarea{padding:.44em .4em .4em 1px;margin-left:6px;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
.widget-hlabel{min-width:10ex;padding-right:8px;padding-top:3px;text-align:right;vertical-align:text-top}
.widget-vlabel{padding-bottom:5px;text-align:center;vertical-align:text-bottom}
.widget-hreadout{padding-left:8px;padding-top:3px;text-align:left;vertical-align:text-top}
.widget-vreadout{padding-top:5px;text-align:center;vertical-align:text-top}
.slide-track{border:1px solid #ccc;background:#fff;border-radius:4px;}
.widget-hslider{padding-left:8px;padding-right:5px;overflow:visible;width:348px;height:5px;max-height:5px;margin-top:11px;margin-bottom:10px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-hslider .ui-slider{border:0 !important;background:none !important;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-hslider .ui-slider .ui-slider-handle{width:14px !important;height:28px !important;margin-top:-8px !important}
.widget-vslider{padding-bottom:8px;overflow:visible;width:5px;max-width:5px;height:250px;margin-left:12px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}.widget-vslider .ui-slider{border:0 !important;background:none !important;margin-left:-4px;margin-top:5px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-vslider .ui-slider .ui-slider-handle{width:28px !important;height:14px !important;margin-left:-9px}
.widget-text{width:350px;margin:0 !important}
.widget-listbox{width:364px;margin-bottom:0}
.widget-numeric-text{width:150px;margin:0 !important}
.widget-progress{width:363px}.widget-progress .bar{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}
.widget-combo-btn{min-width:138px;}
.widget-box{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
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.widget-modal{overflow:hidden;position:absolute !important;top:0;left:0;margin-left:0 !important}
.widget-modal-body{max-height:none !important}
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.docked-widget-modal{overflow:hidden;position:relative !important;top:0 !important;left:0 !important;margin-left:0 !important}
body{background-color:#fff}
body.notebook_app{overflow:hidden}
@media (max-width:767px){body.notebook_app{padding-left:0;padding-right:0}}span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%}
div#notebook_panel{margin:0 0 0 0;padding:0;-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}
div#notebook{font-size:14px;line-height:20px;overflow-y:scroll;overflow-x:auto;width:100%;padding:1em 0 1em 0;margin:0;border-top:1px solid #ababab;outline:none;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
div.ui-widget-content{border:1px solid #ababab;outline:none}
pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:.4em;padding-left:2em}
p.dialog{padding:.2em}
pre,code,kbd,samp{white-space:pre-wrap}
#fonttest{font-family:monospace}
p{margin-bottom:0}
.end_space{height:200px}
.celltoolbar{border:thin solid #cfcfcf;border-bottom:none;background:#eee;border-radius:3px 3px 0 0;width:100%;-webkit-box-pack:end;height:22px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px}
.celltoolbar>div{padding-top:0}
.ctb_global_show .ctb_show.ctb_hideshow{display:block}
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.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top}
.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top}
.celltoolbar label span{font-size:85%}
.celltoolbar input[type=checkbox]{margin:0;margin-left:4px;margin-right:4px}
.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;min-width:30px}
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.completions select{background:#fff;outline:none;border:none;padding:0;margin:0;overflow:auto;font-family:monospace;font-size:110%;color:#000;width:auto}
.completions select option.context{color:#0064cd}
#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0 0 4px 4px}
#menubar .navbar{margin-bottom:8px}
.nav-wrapper{border-bottom:1px solid #d4d4d4}
#menubar li.dropdown{line-height:12px}
i.menu-icon{padding-top:4px}
ul#help_menu li a{overflow:hidden;padding-right:2.2em}ul#help_menu li a i{margin-right:-1.2em}
#notification_area{z-index:10}
.indicator_area{color:#777;padding:4px 3px;margin:0;width:11px;z-index:10;text-align:center}
#kernel_indicator{margin-right:-16px}
.edit_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f040"}
.command_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:' '}
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div#pager_splitter{height:8px}
#pager-container{position:relative;padding:15px 0}
div#pager{font-size:14px;line-height:20px;overflow:auto;display:none}div#pager pre{line-height:1.21429em;color:#000;background-color:#f7f7f7;padding:.4em}
.quickhelp{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
.shortcut_key{display:inline-block;width:20ex;text-align:right;font-family:monospace}
.shortcut_descr{display:inline-block;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
span#save_widget{padding:0 5px;margin-top:12px}
span#checkpoint_status,span#autosave_status{font-size:small}
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.toolbar .btn{padding:2px 8px}
.toolbar .btn-group{margin-top:0}
.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important}
#maintoolbar{margin-bottom:0}
@-moz-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-webkit-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-moz-keyframes fadeIn{from{opacity:0} to{opacity:1}}@-webkit-keyframes fadeIn{from{opacity:0} to{opacity:1}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms}
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.tooltipbuttons{position:absolute;padding-right:15px;top:0;right:0}
.tooltiptext{padding-right:30px}
.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0 6px 10px -1px #adadad;-webkit-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2}.ipython_tooltip a{float:right}
.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7}
.pretooltiparrow{left:0;margin:0;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute}
.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg)}
</style>
<style type="text/css">
.highlight .hll { background-color: #ffffcc }
.highlight { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
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.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
</style>
<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
overflow: visible;
padding: 8px;
}
div#notebook {
overflow: visible;
border-top: none;
}
@media print {
div.cell {
display: block;
page-break-inside: avoid;
}
div.output_wrapper {
display: block;
page-break-inside: avoid;
}
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<h1 id="loan-data-2007-2011-from-lending-club">Loan Data (2007-2011) from Lending Club</h1>
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<p>I used Lending Club to extract loan information.</p>
<p>See the data at https://www.lendingclub.com/info/download-data.action</p>
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<h3 id="whats-a-good-predictor-for-whether-someone-will-pay-off-their-loan-or-not">What's a good predictor for whether someone will pay off their loan or not?</h3>
<p>My prediction is that the length of employment, the amount funded and/or annual income will help to determine loan status.</p>
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In&nbsp;[339]:
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<div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> <span class="n">inline</span>
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<h1 id="import-csv-file">Import CSV file</h1>
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In&nbsp;[340]:
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<div class="highlight"><pre><span class="n">url</span> <span class="o">=</span> <span class="s">&#39;/Users/olehdubno/Desktop/python_tests/LoanStats3b2.csv&#39;</span>
<span class="n">loan</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">low_memory</span> <span class="o">=</span> <span class="bp">False</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">loan_2</span> <span class="o">=</span> <span class="n">loan</span><span class="p">[[</span><span class="s">&#39;funded_amnt&#39;</span><span class="p">,</span><span class="s">&#39;emp_length&#39;</span><span class="p">,</span><span class="s">&#39;annual_inc&#39;</span><span class="p">,</span><span class="s">&#39;loan_status&#39;</span><span class="p">,</span><span class="s">&#39;home_ownership&#39;</span><span class="p">,</span><span class="s">&#39;addr_state&#39;</span><span class="p">,</span><span class="s">&#39;tax_liens&#39;</span><span class="p">,</span><span class="s">&#39;grade&#39;</span><span class="p">]]</span>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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Out[342]:</div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>funded_amnt</th>
<th>emp_length</th>
<th>annual_inc</th>
<th>loan_status</th>
<th>home_ownership</th>
<th>addr_state</th>
<th>tax_liens</th>
<th>grade</th>
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<th>0</th>
<td> 24000</td>
<td> 10+ years</td>
<td> 100000</td>
<td> Current</td>
<td> MORTGAGE</td>
<td> MI</td>
<td> 0</td>
<td> B</td>
</tr>
<tr>
<th>1</th>
<td> 11100</td>
<td> 10+ years</td>
<td> 90000</td>
<td> Current</td>
<td> MORTGAGE</td>
<td> NY</td>
<td> 0</td>
<td> C</td>
</tr>
<tr>
<th>2</th>
<td> 12000</td>
<td> 3 years</td>
<td> 96500</td>
<td> Current</td>
<td> MORTGAGE</td>
<td> TX</td>
<td> 0</td>
<td> A</td>
</tr>
<tr>
<th>3</th>
<td> 15000</td>
<td> 10+ years</td>
<td> 98000</td>
<td> Fully Paid</td>
<td> RENT</td>
<td> NY</td>
<td> 0</td>
<td> C</td>
</tr>
<tr>
<th>4</th>
<td> 27600</td>
<td> 6 years</td>
<td> 73000</td>
<td> Current</td>
<td> MORTGAGE</td>
<td> CO</td>
<td> 0</td>
<td> D</td>
</tr>
</tbody>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>
&lt;class &apos;pandas.core.frame.DataFrame&apos;&gt;
Int64Index: 188127 entries, 0 to 188126
Data columns (total 8 columns):
funded_amnt 188123 non-null float64
emp_length 188123 non-null object
annual_inc 188123 non-null float64
loan_status 188123 non-null object
home_ownership 188123 non-null object
addr_state 188123 non-null object
tax_liens 188123 non-null float64
grade 188123 non-null object
dtypes: float64(3), object(5)
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<h4 id="dropping-na-values-its-only-4-rows-and-not-very-significant">Dropping N/A values (It's only 4 rows and not very significant)</h4>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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&lt;class &apos;pandas.core.frame.DataFrame&apos;&gt;
Int64Index: 188123 entries, 0 to 188122
Data columns (total 8 columns):
funded_amnt 188123 non-null float64
emp_length 188123 non-null object
annual_inc 188123 non-null float64
loan_status 188123 non-null object
home_ownership 188123 non-null object
addr_state 188123 non-null object
tax_liens 188123 non-null float64
grade 188123 non-null object
dtypes: float64(3), object(5)
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<p>We will be focusing on determing the features that cause people to default on their loans.</p>
<p>The loan status column has 7 items. We will leave Current and Fully Paid. The rest of the columns will be grouped as Unpaid.</p>
<p>Which features are representative of a person not paying their loan on time?</p>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">loan_status</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">30</span><span class="p">)</span>
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&lt;matplotlib.axes.AxesSubplot at 0x13788aa50&gt;
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<h1 id="cleaning-data">Cleaning Data</h1>
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<h4 id="using-the-map-function-to-numerate-the-items-of-the-argument-sequence-for-loan-status-and-grade.">Using the map function to numerate the items of the argument sequence for loan status and grade.</h4>
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In&nbsp;[346]:
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<div class="highlight"><pre><span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;loan_status&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s">&#39;Current&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s">&#39;Fully Paid&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s">&#39;Charged Off&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s">&#39;Late(31-120 days)&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s">&#39;In Grace Period&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s">&#39;Late(16-30 days)&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s">&#39;Default&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">})</span>
<span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;grade_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;grade&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s">&#39;A&#39;</span><span class="p">:</span><span class="mi">7</span><span class="p">,</span><span class="s">&#39;B&#39;</span><span class="p">:</span><span class="mi">6</span><span class="p">,</span><span class="s">&#39;C&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s">&#39;D&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s">&#39;E&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s">&#39;F&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s">&#39;G&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>
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<p>Majority of my data is in Current. However, that's not our focus, we want to focus on paid and unpaid.</p>
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In&nbsp;[347]:
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">loan_status_clean</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Current Paid Unpaid&#39;</span><span class="p">)</span>
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Out[347]:</div>
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&lt;matplotlib.text.Text at 0x13834b650&gt;
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SVPl/wMcLoqjl2jI+gAAAABJRU5ErkJggg==
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<h4 id="removing-the-current-status-2-column.-interested-in-determing-whats-responsible-for-people-paying-off-or-defaulting-on-their-loans.">Removing the Current Status '2' column. Interested in determing what's responsible for people paying off or defaulting on their loans.</h4>
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<p>Removing the &quot;Current&quot; status information will gravely limit our data. However, it'll still be pretty good granted we'll have around 54,000 rows still left.</p>
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<div class="highlight"><pre><span class="n">loan_2</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">loan_status_clean</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">]</span>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="p">[</span><span class="s">&quot;loan_status_clean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&quot;loan_status_clean&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">loan_status_clean</span><span class="p">:</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">loan_status_clean</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span><span class="p">)</span>
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<h4 id="cleaning-up-the-employment-length-column-and-removing-years-and-year.">Cleaning up the Employment Length Column and removing '&lt;', '+', 'years' and 'year'.</h4>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;+&#39;</span><span class="p">,</span><span class="s">&#39;&#39;</span><span class="p">)</span>
<span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;&lt;&#39;</span><span class="p">,</span><span class="s">&#39;&#39;</span><span class="p">)</span>
<span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;years&#39;</span><span class="p">,</span><span class="s">&#39;&#39;</span><span class="p">)</span>
<span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;year&#39;</span><span class="p">,</span><span class="s">&#39;&#39;</span><span class="p">)</span>
<span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;n/a&#39;</span><span class="p">,</span><span class="s">&#39;0&#39;</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
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array([&apos;10 &apos;, &apos;2 &apos;, &apos;5 &apos;, &apos;1 &apos;, &apos;9 &apos;, &apos; 1 &apos;, &apos;8 &apos;, &apos;0&apos;, &apos;7 &apos;, &apos;4 &apos;, &apos;3 &apos;,
&apos;6 &apos;, nan], dtype=object)
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<h4 id="converting-my-already-cleaned-employment-length-column-to-a-float.">Converting my already cleaned employment length column to a float.</h4>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;emp_length_clean&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
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<h4 id="substituting-mean-values-for-nan-in-relevant-columns.">Substituting mean values for NaN in relevant columns.</h4>
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<div class="highlight"><pre><span class="n">funded_amnt</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">funded_amnt</span>
<span class="n">mean_funded_amnt</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">funded_amnt</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">funded_amnt</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">funded_amnt</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">mean_funded_amnt</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">annual_inc</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">annual_inc</span>
<span class="n">mean_annual_inc</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">annual_inc</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">annual_inc</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">annual_inc</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">mean_annual_inc</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">emp_length</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span>
<span class="n">mean_emp_length_clean</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">mean_emp_length_clean</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">grade</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">grade</span>
<span class="n">mean_grade_clean</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">grade</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">grade_clean</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">grade_clean</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">mean_grade_clean</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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<h2 id="running-logistic-regression">Running Logistic Regression</h2>
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<div class="highlight"><pre><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="kn">as</span> <span class="nn">sm</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span><span class="p">,</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
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<div class="highlight"><pre><span class="n">X_Variables</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;funded_amnt&#39;</span><span class="p">,</span> <span class="s">&#39;annual_inc&#39;</span><span class="p">,</span> <span class="s">&#39;emp_length_clean&#39;</span><span class="p">,</span> <span class="s">&#39;grade_clean&#39;</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">X_Variables</span><span class="p">]</span>
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<div class="highlight"><pre><span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">values</span>
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<div class="highlight"><pre><span class="n">y</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<div class="highlight"><pre><span class="n">clf</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">()</span>
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<div class="highlight"><pre><span class="n">model</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
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0.77943365368713136
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<div class="highlight"><pre><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">X_Variables</span><span class="p">,</span><span class="n">model</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
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<th>0</th>
<th>1</th>
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<th>0</th>
<td> funded_amnt</td>
<td> [-1.49218373653e-05]</td>
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<th>1</th>
<td> annual_inc</td>
<td> [2.02376963817e-05]</td>
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<th>2</th>
<td> emp_length_clean</td>
<td> [2.93841504621e-08]</td>
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<th>3</th>
<td> grade_clean</td>
<td> [5.18210343946e-08]</td>
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<h2 id="train-test-split">Train test split</h2>
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<div class="highlight"><pre><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>
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LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty=&apos;l2&apos;, random_state=None, tol=0.0001)
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<div class="highlight"><pre><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>
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0.78029597687068164
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<div class="highlight"><pre><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span><span class="n">Y_test</span><span class="p">)</span>
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0.77684674751929439
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<h2 id="decision-tree">Decision Tree</h2>
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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span><span class="n">min_samples_leaf</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>
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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="k">def</span> <span class="nf">measure_performance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">clf</span><span class="p">,</span> <span class="n">show_accuracy</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">show_classification_report</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">show_confusion_matrix</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="n">y_pred</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">show_accuracy</span><span class="p">:</span>
<span class="k">print</span> <span class="s">&quot;Accuracy:{0:.3f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">y_pred</span><span class="p">)),</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span>
<span class="k">if</span> <span class="n">show_classification_report</span><span class="p">:</span>
<span class="k">print</span> <span class="s">&quot;Classification report&quot;</span>
<span class="k">print</span> <span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">y_pred</span><span class="p">),</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span>
<span class="k">if</span> <span class="n">show_confusion_matrix</span><span class="p">:</span>
<span class="k">print</span> <span class="s">&quot;Confusion matrix&quot;</span>
<span class="k">print</span> <span class="n">metrics</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">y_pred</span><span class="p">),</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">&quot;</span>
<span class="n">measure_performance</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">,</span><span class="n">clf</span><span class="p">,</span> <span class="n">show_classification_report</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">show_confusion_matrix</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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Accuracy:0.780
Classification report
precision recall f1-score support
0 0.00 0.00 0.00 8967
1 0.78 1.00 0.88 31847
avg / total 0.61 0.78 0.68 40814
Confusion matrix
[[ 0 8967]
[ 0 31847]]
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<p>According to the confusion matrix, 9,074 of the loans are predicted as unpaid and 31,767 of loans are predicted as paid.</p>
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<h2 id="kfold">KFold</h2>
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<p>Creating a kfold function</p>
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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">KFold</span>
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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">cross_val_score</span><span class="p">,</span> <span class="n">LeaveOneOut</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">sem</span>
<span class="k">def</span> <span class="nf">kfold_cv</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">,</span><span class="n">clf</span><span class="p">):</span>
<span class="c"># Perform Leave-One-Out cross validation</span>
<span class="c"># We are performing 1313 classifications!</span>
<span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">scores</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">for</span> <span class="n">train_index</span><span class="p">,</span><span class="n">test_index</span> <span class="ow">in</span> <span class="n">kf</span><span class="p">:</span>
<span class="n">X_train_cv</span><span class="p">,</span> <span class="n">X_test_cv</span><span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="n">y_train_cv</span><span class="p">,</span> <span class="n">y_test_cv</span><span class="o">=</span> <span class="n">Y_train</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">Y_train</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_cv</span><span class="p">,</span><span class="n">y_train_cv</span><span class="p">)</span>
<span class="n">y_pred</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_cv</span><span class="p">)</span>
<span class="n">scores</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span><span class="o">=</span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test_cv</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span>
<span class="k">print</span> <span class="p">(</span><span class="s">&quot;Mean score: {0:.3f} (+/-{1:.3f})&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">scores</span><span class="p">),</span> <span class="n">sem</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
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<div class="highlight"><pre><span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>
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<h4 id="our-kfold-model-predictive-accuracy-is-77.9">Our kfold model predictive accuracy is 77.9%</h4>
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<div class="highlight"><pre><span class="n">kfold_cv</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">clf</span><span class="p">)</span>
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Mean score: 0.779 (+/-0.000)
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<div class="highlight"><pre><span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
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0.77943365368713136
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<h3 id="roc-and-area-under-the-curve-auc">ROC and Area Under the Curve (AUC)</h3>
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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
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<div class="highlight"><pre><span class="n">predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_train</span><span class="p">)]</span>
<span class="n">fpr_p</span><span class="p">,</span> <span class="n">tpr_p</span><span class="p">,</span> <span class="n">thresholds_p</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span><span class="n">predictions</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s">&#39;AUC for Decision Tree Classifier Predicting Loans Paid&#39;</span><span class="p">)</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">&#39;false positive rate&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;true positive rate&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_p</span><span class="p">,</span> <span class="n">tpr_p</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">&#39;false positive rate&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;true positive rate&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;False-positive rate:&quot;</span><span class="p">,</span> <span class="n">fpr</span>
<span class="k">print</span> <span class="s">&quot;True-positive rate: &quot;</span><span class="p">,</span> <span class="n">tpr</span>
<span class="k">print</span> <span class="s">&quot;Thresholds: &quot;</span><span class="p">,</span> <span class="n">thresholds</span>
<span class="k">print</span> <span class="n">fig</span>
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False-positive rate: [ 0. 1.]
True-positive rate: [ 0. 1.]
Thresholds: [2 1]
Figure(800x320)
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<h2 id="running-the-logistic-regression-against-home-ownership">Running the Logistic Regression against Home Ownership</h2>
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<h4 id="creating-dummies-for-home-ownership-to-better-understand-if-one-status-of-home-ownership-is-more-predictive-of-someone-paying-back-their-loan.">Creating dummies for home ownership to better understand if one status of home ownership is more predictive of someone paying back their loan.</h4>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">home_ownership</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
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Out[375]:</div>
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[&apos;RENT&apos;, &apos;MORTGAGE&apos;, &apos;OWN&apos;, &apos;NONE&apos;, &apos;OTHER&apos;, nan]
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In&nbsp;[376]:
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<div class="highlight"><pre><span class="n">home_ownership</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">loan_2</span><span class="o">.</span><span class="n">home_ownership</span><span class="p">)</span>
<span class="n">loan_2</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">home_ownership</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">X_Variables_2</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;RENT&#39;</span><span class="p">,</span> <span class="s">&#39;MORTGAGE&#39;</span><span class="p">,</span> <span class="s">&#39;OWN&#39;</span><span class="p">,</span> <span class="s">&#39;NONE&#39;</span><span class="p">,</span> <span class="s">&#39;OTHER&#39;</span><span class="p">]</span>
<span class="n">X_2</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">X_Variables_2</span><span class="p">]</span>
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<div class="highlight"><pre><span class="n">X_2</span> <span class="o">=</span> <span class="n">X_2</span><span class="o">.</span><span class="n">values</span>
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In&nbsp;[379]:
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<div class="highlight"><pre><span class="n">y_2</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<div class="highlight"><pre><span class="n">clf</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">()</span>
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<div class="highlight"><pre><span class="n">model_2</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_2</span><span class="p">,</span><span class="n">y_2</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">model_2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_2</span><span class="p">,</span><span class="n">y_2</span><span class="p">)</span>
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0.77943365368713136
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<div class="highlight"><pre><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">X_Variables_2</span><span class="p">,</span><span class="n">model_2</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
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<th>0</th>
<th>1</th>
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<td> RENT</td>
<td> [0.0310973077512]</td>
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<td> MORTGAGE</td>
<td> [0.372819013643]</td>
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<td> OWN</td>
<td> [0.135355977702]</td>
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<td> NONE</td>
<td> [0.290465054095]</td>
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<td> OTHER</td>
<td> [-0.794573804391]</td>
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<p>'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.</p>
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<h2 id="running-the-logistic-regression-against-years-of-employment">Running the Logistic Regression against years of employment</h2>
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<h4 id="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.">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.</h4>
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<div class="highlight"><pre><span class="n">emp_dummies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length</span><span class="p">)</span>
<span class="n">loan_2</span> <span class="o">=</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">emp_dummies</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
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[&apos;10+ years&apos;,
&apos;2 years&apos;,
&apos;5 years&apos;,
&apos;1 year&apos;,
&apos;9 years&apos;,
&apos;&lt; 1 year&apos;,
&apos;8 years&apos;,
&apos;n/a&apos;,
&apos;7 years&apos;,
&apos;4 years&apos;,
&apos;3 years&apos;,
&apos;6 years&apos;,
nan]
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<div class="highlight"><pre><span class="n">X_Variables_3</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;&lt; 1 year&#39;</span><span class="p">,</span><span class="s">&#39;1 year&#39;</span><span class="p">,</span><span class="s">&#39;2 years&#39;</span><span class="p">,</span><span class="s">&#39;3 years&#39;</span><span class="p">,</span><span class="s">&#39;4 years&#39;</span><span class="p">,</span><span class="s">&#39;5 years&#39;</span><span class="p">,</span><span class="s">&#39;6 years&#39;</span><span class="p">,</span><span class="s">&#39;7 years&#39;</span><span class="p">,</span><span class="s">&#39;8 years&#39;</span><span class="p">,</span><span class="s">&#39;9 years&#39;</span><span class="p">,</span><span class="s">&#39;10+ years&#39;</span><span class="p">]</span>
<span class="n">X_3</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="n">X_Variables_3</span><span class="p">]</span>
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<div class="highlight"><pre><span class="n">X_3</span> <span class="o">=</span> <span class="n">X_3</span><span class="o">.</span><span class="n">values</span>
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<div class="highlight"><pre><span class="n">y_3</span> <span class="o">=</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<div class="highlight"><pre><span class="n">clf</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">()</span>
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<div class="highlight"><pre><span class="n">model_3</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_3</span><span class="p">,</span><span class="n">y_3</span><span class="p">)</span>
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<div class="highlight"><pre><span class="n">model_3</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_3</span><span class="p">,</span> <span class="n">y_3</span><span class="p">)</span>
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0.77943365368713136
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<div class="highlight"><pre><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">X_Variables_3</span><span class="p">,</span><span class="n">model</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
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<td> &lt; 1 year</td>
<td> [-1.51966442806e-05]</td>
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<td> 1 year</td>
<td> [2.0332536044e-05]</td>
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<td> 2 years</td>
<td> [8.05831027416e-06]</td>
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<th>3</th>
<td> 3 years</td>
<td> [1.36166767322e-05]</td>
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<h2 id="lets-see-some-graphs">Lets see some graphs</h2>
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<p>As expected only the people without Tax Liens qualify for loans. Not surprisingly.</p>
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<div class="highlight"><pre><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">loan_status_clean</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">tax_liens</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;barh&#39;</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s">&#39;Tax Liens&#39;</span><span class="p">)</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">loan_status_clean</span><span class="p">[</span><span class="n">loan_2</span><span class="o">.</span><span class="n">tax_liens</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;barh&#39;</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s">&#39;#FA2379&#39;</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s">&#39;No Tax Liens&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">&#39;best&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Paid Loan Unpaid Loan&#39;</span><span class="p">)</span>
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<p>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.</p>
<p>And interestingly the amount of the loan doesn't really effect whether it'll be paid off.</p>
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In&nbsp;[394]:
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<div class="highlight"><pre><span class="n">loan_2</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s">&#39;annual_inc&#39;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">,</span><span class="n">grid</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s">&#39;funded_amnt&#39;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s">&#39;loan_status_clean&#39;</span><span class="p">,</span><span class="n">grid</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">40000</span><span class="p">)</span>
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Out[394]:</div>
<div class="output_text output_subarea output_pyout">
<pre>
(0, 40000)
</pre>
</div>
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<p>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.</p>
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In&nbsp;[395]:
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<div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;annual_inc&#39;</span><span class="p">],</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;funded_amnt&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&quot;Plotting Annual Income against Funded Amount&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Funded Amount&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Annual Income&#39;</span><span class="p">)</span>
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Out[395]:</div>
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<p>There are several outliers skewing our data. Lets limit our scatter plot by annual income.</p>
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In&nbsp;[396]:
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<div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;annual_inc&#39;</span><span class="p">],</span> <span class="n">loan_2</span><span class="p">[</span><span class="s">&#39;funded_amnt&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">100000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&quot;Plotting Annual Income against Funded Amount&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Funded Amount&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Annual Income&#39;</span><span class="p">)</span>
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Out[396]:</div>
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&lt;matplotlib.text.Text at 0x116dbd810&gt;
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<p>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.</p>
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In&nbsp;[422]:
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<div class="highlight"><pre><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">1600</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="o">.</span><span class="mi">65</span>
<span class="n">loan_2</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s">&#39;addr_state&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">loan_status_clean</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">)</span>
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Out[422]:</div>
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&lt;matplotlib.axes.AxesSubplot at 0x127662390&gt;
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<p>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.</p>
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<div class="highlight"><pre><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">loan_2</span><span class="o">.</span><span class="n">annual_inc</span><span class="p">,</span> <span class="n">loan_2</span><span class="o">.</span><span class="n">emp_length_clean</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">loan_2</span><span class="o">.</span><span class="n">funded_amnt</span><span class="o">/</span><span class="mi">500</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.005</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Annual Income&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Years Employed&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">100000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>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.</p>
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<p>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.</p>
<p>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.</p>
<p>The larger the loan, the lower the chance of paying it back. Suprise! :)</p>
<p>On average if the person is making 20,000 or more, they're more likelier to pay off their loan.</p>
<p>Moving forward, I would like to dig deeper and do more analysis by state. |</p>
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