Week 1/docs/Scikit_learn_example.html

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.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
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input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
.form-actions:after{clear:both}
.help-block,.help-inline{color:#262626}
.help-block{display:block;margin-bottom:10px}
.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
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.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{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}
.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{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}
.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{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}
.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{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}
.input-prepend.input-append .btn-group:first-child{margin-left:0}
input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
.control-group{margin-bottom:10px}
legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
.form-horizontal .control-group:after{clear:both}
.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
.form-horizontal .help-block{margin-bottom:0}
.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
.form-horizontal .form-actions{padding-left:180px}
table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
.table th{font-weight:bold}
.table thead th{vertical-align:bottom}
.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
.table tbody+tbody{border-top:2px solid #ddd}
.table .table{background-color:#fff}
.table-condensed th,.table-condensed td{padding:4px 5px}
.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
.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}
.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
.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}
.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
.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}
table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
.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}
.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
.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}
[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
.icon-glass{background-position:0 0}
.icon-music{background-position:-24px 0}
.icon-search{background-position:-48px 0}
.icon-envelope{background-position:-72px 0}
.icon-heart{background-position:-96px 0}
.icon-star{background-position:-120px 0}
.icon-star-empty{background-position:-144px 0}
.icon-user{background-position:-168px 0}
.icon-film{background-position:-192px 0}
.icon-th-large{background-position:-216px 0}
.icon-th{background-position:-240px 0}
.icon-th-list{background-position:-264px 0}
.icon-ok{background-position:-288px 0}
.icon-remove{background-position:-312px 0}
.icon-zoom-in{background-position:-336px 0}
.icon-zoom-out{background-position:-360px 0}
.icon-off{background-position:-384px 0}
.icon-signal{background-position:-408px 0}
.icon-cog{background-position:-432px 0}
.icon-trash{background-position:-456px 0}
.icon-home{background-position:0 -24px}
.icon-file{background-position:-24px -24px}
.icon-time{background-position:-48px -24px}
.icon-road{background-position:-72px -24px}
.icon-download-alt{background-position:-96px -24px}
.icon-download{background-position:-120px -24px}
.icon-upload{background-position:-144px -24px}
.icon-inbox{background-position:-168px -24px}
.icon-play-circle{background-position:-192px -24px}
.icon-repeat{background-position:-216px -24px}
.icon-refresh{background-position:-240px -24px}
.icon-list-alt{background-position:-264px -24px}
.icon-lock{background-position:-287px -24px}
.icon-flag{background-position:-312px -24px}
.icon-headphones{background-position:-336px -24px}
.icon-volume-off{background-position:-360px -24px}
.icon-volume-down{background-position:-384px -24px}
.icon-volume-up{background-position:-408px -24px}
.icon-qrcode{background-position:-432px -24px}
.icon-barcode{background-position:-456px -24px}
.icon-tag{background-position:0 -48px}
.icon-tags{background-position:-25px -48px}
.icon-book{background-position:-48px -48px}
.icon-bookmark{background-position:-72px -48px}
.icon-print{background-position:-96px -48px}
.icon-camera{background-position:-120px -48px}
.icon-font{background-position:-144px -48px}
.icon-bold{background-position:-167px -48px}
.icon-italic{background-position:-192px -48px}
.icon-text-height{background-position:-216px -48px}
.icon-text-width{background-position:-240px -48px}
.icon-align-left{background-position:-264px -48px}
.icon-align-center{background-position:-288px -48px}
.icon-align-right{background-position:-312px -48px}
.icon-align-justify{background-position:-336px -48px}
.icon-list{background-position:-360px -48px}
.icon-indent-left{background-position:-384px -48px}
.icon-indent-right{background-position:-408px -48px}
.icon-facetime-video{background-position:-432px -48px}
.icon-picture{background-position:-456px -48px}
.icon-pencil{background-position:0 -72px}
.icon-map-marker{background-position:-24px -72px}
.icon-adjust{background-position:-48px -72px}
.icon-tint{background-position:-72px -72px}
.icon-edit{background-position:-96px -72px}
.icon-share{background-position:-120px -72px}
.icon-check{background-position:-144px -72px}
.icon-move{background-position:-168px -72px}
.icon-step-backward{background-position:-192px -72px}
.icon-fast-backward{background-position:-216px -72px}
.icon-backward{background-position:-240px -72px}
.icon-play{background-position:-264px -72px}
.icon-pause{background-position:-288px -72px}
.icon-stop{background-position:-312px -72px}
.icon-forward{background-position:-336px -72px}
.icon-fast-forward{background-position:-360px -72px}
.icon-step-forward{background-position:-384px -72px}
.icon-eject{background-position:-408px -72px}
.icon-chevron-left{background-position:-432px -72px}
.icon-chevron-right{background-position:-456px -72px}
.icon-plus-sign{background-position:0 -96px}
.icon-minus-sign{background-position:-24px -96px}
.icon-remove-sign{background-position:-48px -96px}
.icon-ok-sign{background-position:-72px -96px}
.icon-question-sign{background-position:-96px -96px}
.icon-info-sign{background-position:-120px -96px}
.icon-screenshot{background-position:-144px -96px}
.icon-remove-circle{background-position:-168px -96px}
.icon-ok-circle{background-position:-192px -96px}
.icon-ban-circle{background-position:-216px -96px}
.icon-arrow-left{background-position:-240px -96px}
.icon-arrow-right{background-position:-264px -96px}
.icon-arrow-up{background-position:-289px -96px}
.icon-arrow-down{background-position:-312px -96px}
.icon-share-alt{background-position:-336px -96px}
.icon-resize-full{background-position:-360px -96px}
.icon-resize-small{background-position:-384px -96px}
.icon-plus{background-position:-408px -96px}
.icon-minus{background-position:-433px -96px}
.icon-asterisk{background-position:-456px -96px}
.icon-exclamation-sign{background-position:0 -120px}
.icon-gift{background-position:-24px -120px}
.icon-leaf{background-position:-48px -120px}
.icon-fire{background-position:-72px -120px}
.icon-eye-open{background-position:-96px -120px}
.icon-eye-close{background-position:-120px -120px}
.icon-warning-sign{background-position:-144px -120px}
.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}
.btn-mini .caret,.btn-small .caret{margin-top:8px}
.dropup .btn-large .caret{border-bottom-width:5px}
.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
.btn-group-vertical>.btn:last-child{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}
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.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}
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.alert-danger h4,.alert-error h4{color:#b94a48}
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.alert-block p+p{margin-top:5px}
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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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.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
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.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)}
.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;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-warning .bar,.progress .bar-warning{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)}
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.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
.carousel-caption h4{margin:0 0 5px}
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.hero-unit li{line-height:30px}
.pull-right{float:right}
.pull-left{float:left}
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.show{display:block}
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.affix{position:fixed}
@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
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.visible-tablet{display:none !important}
.hidden-desktop{display:none !important}
.visible-desktop{display:inherit !important}
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.rendered_html *+p{margin-top:1em}
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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)}
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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}
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p{margin-bottom:0}
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.celltoolbar>div{padding-top:0}
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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}
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.completions select option.context{color:#0064cd}
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#menubar .navbar{margin-bottom:8px}
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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}
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div#pager_splitter{height:8px}
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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}
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.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}
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    </style>
<style type="text/css">
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.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 */
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<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; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration -->

</head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">
In&nbsp;[1]:
</div>
<div class="inner_cell">
    <div class="input_area">
<div class="highlight"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="n">Image</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&#39;http://python.org/images/python-logo.gif&#39;</span><span class="p">)</span>

<span class="c"># helpful links:</span>
<span class="c"># http://scikit-learn.org/stable/</span>
<span class="c"># http://pandas.pydata.org/pandas-docs/stable/cookbook.html</span>
<span class="c"># http://pandas.pydata.org/pandas-docs/stable/visualization.html</span>
<span class="c"># http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_7-Plotting-with-Pandas.ipynb</span>
<span class="c"># http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10</span>
<span class="c"># http://pandas.pydata.org/pandas-docs/stable/basics.html</span>
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<div class="highlight"><pre><span class="c"># import modules into namespace</span>
<span class="o">%</span><span class="k">matplotlib</span> <span class="n">inline</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<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">scipy</span> <span class="kn">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="kn">as</span> <span class="nn">ss</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</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><span class="p">,</span> <span class="n">cross_val_score</span><span class="p">,</span> <span class="n">KFold</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span><span class="p">,</span> <span class="n">auc</span><span class="p">,</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span> 
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
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<div class="highlight"><pre><span class="c"># read in and visualize data</span>
<span class="n">filename</span> <span class="o">=</span> <span class="s">&quot;C:\Test\Churn_Calls.csv&quot;</span>
<span class="n">df</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">filename</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s">&#39;,&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>state</th>
      <th>account_length</th>
      <th>area_code</th>
      <th>international_plan</th>
      <th>voice_mail_plan</th>
      <th>number_vmail_messages</th>
      <th>total_day_minutes</th>
      <th>total_day_calls</th>
      <th>total_day_charge</th>
      <th>total_eve_minutes</th>
      <th>total_eve_calls</th>
      <th>total_eve_charge</th>
      <th>total_night_minutes</th>
      <th>total_night_calls</th>
      <th>total_night_charge</th>
      <th>total_intl_minutes</th>
      <th>total_intl_calls</th>
      <th>total_intl_charge</th>
      <th>number_customer_service_calls</th>
      <th>churn</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td> AK</td>
      <td>  1</td>
      <td> area_code_408</td>
      <td>  no</td>
      <td>  no</td>
      <td>  0</td>
      <td> 175.2</td>
      <td>  74</td>
      <td> 29.78</td>
      <td> 151.7</td>
      <td>  79</td>
      <td> 12.89</td>
      <td> 230.5</td>
      <td> 109</td>
      <td> 10.37</td>
      <td>  5.3</td>
      <td> 3</td>
      <td> 1.43</td>
      <td> 1</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>1</th>
      <td> AK</td>
      <td> 36</td>
      <td> area_code_408</td>
      <td>  no</td>
      <td> yes</td>
      <td> 30</td>
      <td> 146.3</td>
      <td> 128</td>
      <td> 24.87</td>
      <td> 162.5</td>
      <td>  80</td>
      <td> 13.81</td>
      <td> 129.3</td>
      <td> 109</td>
      <td>  5.82</td>
      <td> 14.5</td>
      <td> 6</td>
      <td> 3.92</td>
      <td> 0</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>2</th>
      <td> AK</td>
      <td> 36</td>
      <td> area_code_415</td>
      <td> yes</td>
      <td> yes</td>
      <td> 19</td>
      <td> 171.9</td>
      <td>  96</td>
      <td> 29.22</td>
      <td> 198.4</td>
      <td> 111</td>
      <td> 16.86</td>
      <td> 321.7</td>
      <td>  76</td>
      <td> 14.48</td>
      <td> 10.5</td>
      <td> 1</td>
      <td> 2.84</td>
      <td> 1</td>
      <td> yes</td>
    </tr>
    <tr>
      <th>3</th>
      <td> AK</td>
      <td> 41</td>
      <td> area_code_415</td>
      <td>  no</td>
      <td>  no</td>
      <td>  0</td>
      <td> 159.3</td>
      <td>  66</td>
      <td> 27.08</td>
      <td> 125.9</td>
      <td>  75</td>
      <td> 10.70</td>
      <td> 261.9</td>
      <td>  76</td>
      <td> 11.79</td>
      <td> 11.1</td>
      <td> 5</td>
      <td> 3.00</td>
      <td> 1</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>4</th>
      <td> AK</td>
      <td> 42</td>
      <td> area_code_415</td>
      <td>  no</td>
      <td>  no</td>
      <td>  0</td>
      <td> 171.0</td>
      <td> 129</td>
      <td> 29.07</td>
      <td> 183.9</td>
      <td>  96</td>
      <td> 15.63</td>
      <td> 130.2</td>
      <td>  90</td>
      <td>  5.86</td>
      <td>  4.6</td>
      <td> 6</td>
      <td> 1.24</td>
      <td> 0</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>5</th>
      <td> AK</td>
      <td> 48</td>
      <td> area_code_415</td>
      <td>  no</td>
      <td> yes</td>
      <td> 37</td>
      <td> 211.7</td>
      <td> 115</td>
      <td> 35.99</td>
      <td> 159.9</td>
      <td>  84</td>
      <td> 13.59</td>
      <td> 144.1</td>
      <td>  80</td>
      <td>  6.48</td>
      <td> 12.2</td>
      <td> 1</td>
      <td> 3.29</td>
      <td> 1</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>6</th>
      <td> AK</td>
      <td> 50</td>
      <td> area_code_408</td>
      <td>  no</td>
      <td>  no</td>
      <td>  0</td>
      <td> 183.6</td>
      <td> 107</td>
      <td> 31.21</td>
      <td>  58.6</td>
      <td> 118</td>
      <td>  4.98</td>
      <td> 202.6</td>
      <td>  99</td>
      <td>  9.12</td>
      <td>  8.7</td>
      <td> 3</td>
      <td> 2.35</td>
      <td> 1</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>7</th>
      <td> AK</td>
      <td> 51</td>
      <td> area_code_510</td>
      <td> yes</td>
      <td> yes</td>
      <td> 12</td>
      <td> 135.8</td>
      <td>  60</td>
      <td> 23.09</td>
      <td> 200.6</td>
      <td> 134</td>
      <td> 17.05</td>
      <td> 192.4</td>
      <td>  98</td>
      <td>  8.66</td>
      <td> 12.3</td>
      <td> 7</td>
      <td> 3.32</td>
      <td> 2</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>8</th>
      <td> AK</td>
      <td> 52</td>
      <td> area_code_408</td>
      <td>  no</td>
      <td>  no</td>
      <td>  0</td>
      <td> 217.0</td>
      <td> 104</td>
      <td> 36.89</td>
      <td> 152.3</td>
      <td>  83</td>
      <td> 12.95</td>
      <td> 134.3</td>
      <td> 109</td>
      <td>  6.04</td>
      <td> 11.8</td>
      <td> 4</td>
      <td> 3.19</td>
      <td> 2</td>
      <td>  no</td>
    </tr>
    <tr>
      <th>9</th>
      <td> AK</td>
      <td> 52</td>
      <td> area_code_415</td>
      <td>  no</td>
      <td> yes</td>
      <td> 24</td>
      <td> 170.9</td>
      <td>  71</td>
      <td> 29.05</td>
      <td> 201.4</td>
      <td>  80</td>
      <td> 17.12</td>
      <td> 159.0</td>
      <td> 124</td>
      <td>  7.15</td>
      <td>  4.1</td>
      <td> 5</td>
      <td> 1.11</td>
      <td> 2</td>
      <td>  no</td>
    </tr>
  </tbody>
</table>
<p>10 rows × 20 columns</p>
</div>
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<div class="highlight"><pre><span class="c"># designate target variable name</span>
<span class="n">targetName</span> <span class="o">=</span> <span class="s">&#39;churn&#39;</span>
<span class="c"># move target variable into first column</span>
<span class="n">targetSeries</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">targetName</span><span class="p">]</span>
<span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="n">targetName</span><span class="p">]</span>
<span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">targetName</span><span class="p">,</span> <span class="n">targetSeries</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># begin eda</span>
<span class="n">targetEDA</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">targetName</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()[</span><span class="n">targetName</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="n">targetEDA</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">targetEDA</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">grid</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;k&#39;</span><span class="p">)</span>
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churn
no       4293
yes       707
Name: churn, dtype: int64

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&lt;matplotlib.lines.Line2D at 0xe94fcc0&gt;
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In&nbsp;[7]:
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<div class="highlight"><pre><span class="c"># continue eda</span>
<span class="n">categorical_features</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">continuous_features</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
	<span class="n">attName</span> <span class="o">=</span> <span class="n">col</span>
	<span class="n">dType</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span>
	<span class="n">missing</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">])</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
    <span class="c"># create stacked bar charts for categorical variables</span>
	<span class="k">if</span> <span class="p">(</span><span class="n">dType</span> <span class="o">==</span> <span class="s">&#39;object&#39;</span><span class="p">):</span>
		<span class="n">cat_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
		<span class="n">cat_data</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">stacked</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;blue&#39;</span><span class="p">,</span><span class="s">&#39;green&#39;</span><span class="p">],</span> <span class="n">grid</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
		<span class="n">categorical_features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attName</span><span class="p">)</span>
    <span class="c"># create box plots for categorical variables</span>
	<span class="k">else</span><span class="p">:</span>
		<span class="n">df</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="n">attName</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="n">targetName</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
		<span class="n">continuous_features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attName</span><span class="p">)</span>
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"
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In&nbsp;[8]:
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    <div class="input_area">
<div class="highlight"><pre><span class="c"># create a histogram matrix</span>
<span class="n">df</span><span class="p">[</span><span class="n">continuous_features</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">grid</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
</pre></div>

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    Out[8]:</div>


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<pre>
array([[&lt;matplotlib.axes.AxesSubplot object at 0x00000000179BD128&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x000000001689E518&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x0000000016A16DA0&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000179A0390&gt;],
       [&lt;matplotlib.axes.AxesSubplot object at 0x00000000167175F8&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000174CF320&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x0000000015C34C18&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000158EBC88&gt;],
       [&lt;matplotlib.axes.AxesSubplot object at 0x00000000174BF4A8&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x000000000F0A0C18&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000174C3C88&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000174C7B38&gt;],
       [&lt;matplotlib.axes.AxesSubplot object at 0x000000001655F668&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x00000000174CD240&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x000000000EC336D8&gt;,
        &lt;matplotlib.axes.AxesSubplot object at 0x0000000015C2F358&gt;]], dtype=object)
</pre>
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In&nbsp;[9]:
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<div class="highlight"><pre><span class="c"># create a parallel coordinates plot</span>
<span class="kn">from</span> <span class="nn">pandas.tools.plotting</span> <span class="kn">import</span> <span class="n">parallel_coordinates</span>
<span class="n">targetSeries</span> <span class="o">=</span> <span class="n">targetSeries</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;no&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">targetSeries</span> <span class="o">=</span> <span class="n">targetSeries</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;yes&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">tempDF</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">continuous_features</span><span class="p">]</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">targetSeries</span><span class="p">)</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">15</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">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">parallel_coordinates</span><span class="p">(</span><span class="n">tempDF</span><span class="p">,</span> <span class="s">&#39;churn&#39;</span><span class="p">)</span>
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    Out[9]:</div>


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&lt;matplotlib.axes.AxesSubplot at 0x16584da0&gt;
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SUVORK5CYII=
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>
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In&nbsp;[10]:
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<div class="highlight"><pre><span class="c"># create an andrews curve plot</span>
<span class="kn">from</span> <span class="nn">pandas.tools.plotting</span> <span class="kn">import</span> <span class="n">andrews_curves</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">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">andrews_curves</span><span class="p">(</span><span class="n">tempDF</span><span class="p">,</span> <span class="s">&#39;churn&#39;</span><span class="p">)</span>
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    Out[10]:</div>


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<pre>
&lt;matplotlib.axes.AxesSubplot at 0x1624d0f0&gt;
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"
>
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</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">
In&nbsp;[11]:
</div>
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    <div class="input_area">
<div class="highlight"><pre><span class="c"># set baseline model expectation</span>
<span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">targetSeries</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>

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</div>
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<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">
    Out[11]:</div>


<div class="output_text output_subarea output_pyout">
<pre>
0.85860000000000003
</pre>
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<div class="highlight"><pre><span class="c"># binarize target variable</span>
<span class="n">df</span><span class="p">[</span><span class="s">&#39;churn&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">targetSeries</span>
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<div class="highlight"><pre><span class="c"># perform data transformation</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
	<span class="n">attName</span> <span class="o">=</span> <span class="n">col</span>
	<span class="n">dType</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span>
	<span class="n">missing</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">])</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
	<span class="n">uniqueCount</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
	<span class="c"># discretize (create dummies)</span>
	<span class="k">if</span> <span class="n">dType</span> <span class="o">==</span> <span class="nb">object</span><span class="p">:</span>
		<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">prefix</span><span class="o">=</span><span class="n">col</span><span class="p">)],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
		<span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span>
	<span class="c"># standardize</span>
	<span class="c"># normalize</span>
	<span class="c"># scale</span>
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<div class="highlight"><pre><span class="c"># define normal distribution fit function</span>
<span class="k">def</span> <span class="nf">fit_dist</span><span class="p">(</span><span class="n">dfColumn</span><span class="p">):</span>
	<span class="c"># convert series into numpy array</span>
	<span class="n">ndArray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dfColumn</span><span class="p">)</span>
    <span class="c"># fit our data set against normal probability distribution</span>
	<span class="n">parameters</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="s">&quot;ss.norm.fit(ndArray)&quot;</span><span class="p">)</span>
    <span class="c"># applying the Kolmogorov-Smirnof one sided test</span>
 	<span class="n">D</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">kstest</span><span class="p">(</span><span class="n">ndArray</span><span class="p">,</span> <span class="s">&quot;norm&quot;</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
 	<span class="k">if</span> <span class="n">D</span> <span class="o">&lt;</span> <span class="mf">0.02</span> <span class="ow">and</span> <span class="n">p</span> <span class="o">&gt;</span> <span class="mf">0.05</span><span class="p">:</span>
 		<span class="k">return</span> <span class="bp">True</span>
 	<span class="k">else</span><span class="p">:</span>
 		<span class="k">return</span> <span class="bp">False</span>
    
<span class="c"># impute missing values</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
	<span class="n">attName</span> <span class="o">=</span> <span class="n">col</span>
	<span class="n">dType</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span>
	<span class="n">missing</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">])</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
	<span class="n">uniqueCount</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
	<span class="n">distCheck</span> <span class="o">=</span> <span class="n">fit_dist</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">])</span>
	<span class="n">col_mean</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
	<span class="n">col_median</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
	<span class="n">col_mode</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">mode</span><span class="p">()</span>
	<span class="k">if</span> <span class="p">(</span><span class="n">distCheck</span> <span class="o">==</span> <span class="bp">True</span> <span class="ow">and</span> <span class="n">missing</span> <span class="o">==</span> <span class="bp">True</span> <span class="ow">and</span> <span class="n">uniqueCount</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">):</span>
		<span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">col_mean</span><span class="p">)</span>
	<span class="k">if</span> <span class="p">(</span><span class="n">distCheck</span> <span class="o">==</span> <span class="bp">False</span> <span class="ow">and</span> <span class="n">missing</span> <span class="o">==</span> <span class="bp">True</span> <span class="ow">and</span> <span class="n">uniqueCount</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">):</span>
		<span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">col_median</span><span class="p">)</span>
	<span class="k">if</span> <span class="p">(</span><span class="n">distCheck</span> <span class="o">==</span> <span class="bp">False</span> <span class="ow">and</span> <span class="n">missing</span> <span class="o">==</span> <span class="bp">True</span> <span class="ow">and</span> <span class="n">uniqueCount</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">):</span>
		<span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">attName</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">col_mode</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># split dataset into testing and training</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># train model</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">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">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># test model</span>
<span class="n">target_predicted</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">features_test</span><span class="p">)</span>
<span class="n">accuracy_score</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">)</span>
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0.92700000000000005
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<div class="highlight"><pre><span class="c"># display confusion matrix</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">cm</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">&#39;Confusion matrix&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</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;True label&#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;Predicted label&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>
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[[1641   67]
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<div class="highlight"><pre><span class="c"># define roc function</span>
<span class="k">def</span> <span class="nf">plot_roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</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">roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
    <span class="n">roc_auc</span> <span class="o">=</span> <span class="n">auc</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="c"># Plot ROC curve</span>
    <span class="n">plt</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="n">label</span><span class="o">=</span><span class="s">&#39;ROC curve (area = </span><span class="si">%0.3f</span><span class="s">)&#39;</span> <span class="o">%</span> <span class="n">roc_auc</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s">&#39;k--&#39;</span><span class="p">)</span>  <span class="c"># random predictions curve</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</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;False Positive Rate or (1 - Specifity)&#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;True Positive Rate or (Sensitivity)&#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">&#39;Receiver Operating Characteristic&#39;</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">&quot;lower right&quot;</span><span class="p">)</span>

<span class="c"># create roc curve</span>
<span class="n">target_predicted_proba</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>
<span class="n">plot_roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</span><span class="p">)</span>
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<div class="highlight"><pre><span class="c"># print classification report</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="p">[</span><span class="s">&quot;churn = no&quot;</span><span class="p">,</span> <span class="s">&quot;churn = yes&quot;</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">,</span> <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>
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<pre>
             precision    recall  f1-score   support

 churn = no       0.95      0.96      0.96      1708
churn = yes       0.76      0.73      0.74       292

avg / total       0.93      0.93      0.93      2000


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In&nbsp;[21]:
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<div class="highlight"><pre><span class="c"># assess model at different random splits</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">score_0</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">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">score_1</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">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">score_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">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_0</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_2</span><span class="p">)</span>
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0.925
0.919
0.916

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<div class="highlight"><pre><span class="c"># perform proper cross validation</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">accuracy_min1</span><span class="p">,</span> <span class="n">accuracy_mean1</span><span class="p">,</span> <span class="n">accuracy_max1</span> <span class="o">=</span> <span class="n">scores</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">scores</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="n">rocs</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s">&#39;roc_auc&#39;</span><span class="p">)</span>
<span class="n">roc_min1</span><span class="p">,</span> <span class="n">roc_mean1</span><span class="p">,</span> <span class="n">roc_max1</span> <span class="o">=</span> <span class="n">rocs</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">rocs</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">rocs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">rocs</span><span class="p">)</span>
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[ 0.925  0.943  0.919  0.916  0.908]
[ 0.85075009  0.8851006   0.84672925  0.80886602  0.82824453]

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<div class="highlight"><pre><span class="c"># train random forest model</span>
<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span>
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<pre>
RandomForestClassifier(bootstrap=True, compute_importances=None,
            criterion=&apos;gini&apos;, max_depth=None, max_features=&apos;auto&apos;,
            min_density=None, min_samples_leaf=1, min_samples_split=2,
            n_estimators=20, n_jobs=4, oob_score=False, random_state=None,
            verbose=0)
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<div class="highlight"><pre><span class="c"># test random forest model</span>
<span class="n">target_predicted</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>
<span class="n">accuracy_score</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">)</span>
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0.94450000000000001
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<div class="highlight"><pre><span class="c"># display confusion matrix</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">cm</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">&#39;Confusion matrix&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</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;True label&#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;Predicted label&#39;</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>
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[[1695   18]
 [  93  194]]

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<div class="highlight"><pre><span class="c"># define roc function</span>
<span class="k">def</span> <span class="nf">plot_roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</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">roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
    <span class="n">roc_auc</span> <span class="o">=</span> <span class="n">auc</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="c"># Plot ROC curve</span>
    <span class="n">plt</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="n">label</span><span class="o">=</span><span class="s">&#39;ROC curve (area = </span><span class="si">%0.3f</span><span class="s">)&#39;</span> <span class="o">%</span> <span class="n">roc_auc</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s">&#39;k--&#39;</span><span class="p">)</span>  <span class="c"># random predictions curve</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</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;False Positive Rate or (1 - Specifity)&#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;True Positive Rate or (Sensitivity)&#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">&#39;Receiver Operating Characteristic&#39;</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">&quot;lower right&quot;</span><span class="p">)</span>

<span class="c"># create roc curve</span>
<span class="n">target_predicted_proba</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>
<span class="n">plot_roc_curve</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted_proba</span><span class="p">)</span>
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"
>
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</div>
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</div>
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<div class="input">
<div class="prompt input_prompt">
In&nbsp;[27]:
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    <div class="input_area">
<div class="highlight"><pre><span class="c"># print classification report</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="p">[</span><span class="s">&quot;churn = no&quot;</span><span class="p">,</span> <span class="s">&quot;churn = yes&quot;</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">target_test</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">,</span> <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>
</pre></div>

</div>
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</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>
             precision    recall  f1-score   support

 churn = no       0.95      0.99      0.97      1713
churn = yes       0.92      0.68      0.78       287

avg / total       0.94      0.94      0.94      2000


</pre>
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</div>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">
In&nbsp;[28]:
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<div class="highlight"><pre><span class="c"># assess model at different random splits</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">score_0</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">score_1</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="n">features_train</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">target_train</span><span class="p">,</span> <span class="n">target_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.40</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">score_2</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features_train</span><span class="p">,</span> <span class="n">target_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">features_test</span><span class="p">,</span> <span class="n">target_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_0</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">score_2</span><span class="p">)</span>
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0.933
0.942
0.9465

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<div class="highlight"><pre><span class="c"># perform proper cross validation</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">accuracy_min2</span><span class="p">,</span> <span class="n">accuracy_mean2</span><span class="p">,</span> <span class="n">accuracy_max2</span> <span class="o">=</span> <span class="n">scores</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">scores</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="n">rocs</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s">&#39;roc_auc&#39;</span><span class="p">)</span>
<span class="n">roc_min2</span><span class="p">,</span> <span class="n">roc_mean2</span><span class="p">,</span> <span class="n">roc_max2</span> <span class="o">=</span> <span class="n">rocs</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">rocs</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">rocs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">rocs</span><span class="p">)</span>
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[ 0.942  0.959  0.936  0.942  0.93 ]
[ 0.89892998  0.93315252  0.91289971  0.91251354  0.91446288]

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<div class="highlight"><pre><span class="c"># select best model based on mean accuracy and roc</span>
<span class="k">if</span> <span class="n">accuracy_mean1</span> <span class="o">&gt;=</span> <span class="n">accuracy_mean2</span><span class="p">:</span>
	<span class="k">print</span><span class="p">(</span><span class="s">&quot;Model 1 wins on accuracy&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
	<span class="k">print</span><span class="p">(</span><span class="s">&quot;Model 2 wins on accuracy&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">roc_mean1</span> <span class="o">&gt;=</span> <span class="n">roc_mean2</span><span class="p">:</span>
	<span class="k">print</span><span class="p">(</span><span class="s">&quot;Model 1 wins based on roc&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
	<span class="k">print</span><span class="p">(</span><span class="s">&quot;Model 2 wins based on roc&quot;</span><span class="p">)</span>
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Model 2 wins on accuracy
Model 2 wins based on roc

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<div class="highlight"><pre> 
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