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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Haseeb Tariq</title>
<link rel="shortcut icon" type="image/x-icon" href="favicon.ico" />
<link rel="stylesheet" href="fa-all.min.css" />
<link rel="stylesheet" href="bootstrap.min.css">
<link href="fonts.css" rel="stylesheet" type="text/css">
<script src="popper.min.js"></script>
<script src="tippy-bundle.umd.min.js"></script>
<script>
function goBack() {
window.history.back();
}
window.onload = function() {
tippy('.tippy', {
placement: 'bottom',
arrow: false,
});
};
</script>
<style>
.clear-margin {
margin: 0;
}
.space-top {
margin-top: 10px;
}
.space-right {
margin-right: 10px;
}
.icon-left {
margin-right: 5px;
}
.icon-right {
margin-left: 5px;
}
.labels {
word-spacing: 5px;
line-height: 2;
}
.label-keyword {
display: inline-block;
background: #7eb0db;
color: white;
font-size: 0.9em;
padding: 5px;
border: 1px solid #357ebd;
}
.link-disguise {
color: inherit;
}
.link-disguise:hover {
color: inherit;
}
@media (max-width: 992px) {
.clear-margin-sm {
margin-bottom: 0;
}
}
body {
font-family: 'Lato', "Helvetica Neue", Helvetica, Arial, sans-serif;
background: #f0f0f0;
color: #333333;
font-size: 16px;
}
.text-bolder {
font-weight: bold;
}
@media only screen {
.container {
max-width: 50em;
}
}
@media (max-width: 480px) {
ul {
padding-left: 25px;
}
/*
.mobile-title {
display: inline-block;
margin-left: 5px;
font-weight: bold;
text-transform: uppercase;
vertical-align: middle;
}
*/
.background-details {
display: block;
}
.background-details .icon {
max-width: inherit;
min-width: inherit;
text-align: left;
}
.background-details .icon,
.background-details .info {
display: block;
padding: 10px 0;
}
.background-details .title {
display: none;
}
.card-nested {
padding: 5px 0;
}
}
.t-details {
margin-left: 10px;
}
.profile-card {
display: table;
width: 100%;
}
.profile-pic {
display: table-cell;
vertical-align: top;
padding: 1rem 4rem 0 0 ;
}
.profile-pic img {
width: 100px;
height: 100px;
}
.contact-details {
display: table-cell;
}
.contact-details .detail {
display: inline-block;
line-height: 2;
margin-right: 2rem;
}
.contact-details .detail .icon {
padding-right: 7px;
color: #888;
}
.contact-details .detail .info {
font-size: 0.8em;
}
.social-links {
line-height: 2.5;
}
.social-links-offline {
line-height: 2.5;
}
.social-link {
display: initial;
text-align: center;
}
.social-link span {
display: inline-block;
vertical-align: middle;
margin-bottom: 20px;
margin-right: 20px;
}
.social-link:hover,
.social-link:focus {
text-decoration: none;
}
.social-link .fa .fa-brands .fa-solid {
width: 2em;
}
.social-link-offline {
display: block;
}
.social-link-offline span {
display: inline-block;
vertical-align: middle;
}
.social-link-offline .fa .fa-brands .fa-solid {
margin-right: 13px;
}
.background-card h4 {
font-variant: small-caps;
color: #777777;
border-bottom: 1px solid #999999;
padding-bottom: 1em;
margin-bottom: 1em;
line-height: 1.2;
}
.background-card h4:not(:first-child) {
margin-top: 2em;
}
h4 > .fa .fa-brands .fa-solid,
h4 > .title {
display: inline-block;
vertical-align: baseline;
}
h4 > .fa .fa-brands .fa-solid {
width: 4rem;
}
.content {
line-height: 1.5;
}
.card {
background: #ffffff;
border: 1px solid #e6e6e6;
border-radius: 3px;
padding: 2em 3.5em;
}
.card-nested {
padding: 1.5rem 0 1.5rem 4.2rem;
}
.card-wrapper {
padding: 5px;
}
.enumeration:not(:last-child):after {
content: ", ";
}
.enumeration:last-child:after {
content: "";
}
@media only screen and (max-width: 768px) {
.profile-card {
display: block;
}
.contact-details,
.profile-pic {
display: block;
}
.card-nested {
padding-left: 0;
}
}
@media only screen and (max-width: 480px) {
.contact-details .detail {
display: block;
}
}
.card-skills {
position: relative;
}
.skill-info {
margin-left: 20px;
}
@media only screen and (min-width: 768px) {
.skill {
border-left: 9px solid #555555;
}
.skill.beginner {
height: 50%;
border-color: #e74c3c;
}
.skill.intermediate {
height: 70%;
border-color: #f1c40f;
}
.skill.advanced {
border-color: #5cb85c;
}
.skill.master {
border-color: black;
}
}
@media (max-width: 768px) {
.quote {
font-size: inherit;
}
}
@media print {
body {
font-size: 10pt;
}
a[href]:after {
content: none !important;
}
.pagebreak {
page-break-before: always;
}
.background-card h4:not(:first-child) {
margin-top: 0 !important;
}
.card {
border: 0;
padding: 0;
}
.container {
max-width: 100%;
width: 100%;
}
.contact-details .detail .icon {
color: #888 !important;
}
.background-card h4 {
padding-bottom: 0.5em;
margin-bottom: 0.5em;
margin-top: 1em !important;
}
h4 > .fa .fa-brands .fa-solid {
display: none !important;
}
.card-nested {
padding: .5rem 0 .5rem 0;
}
.labels {
display: inline;
}
.skill-info {
margin-left: 0;
}
blockquote {
font-size: 100%;
}
}
@font-face {
font-family: 'icomoon';
src: url('fonts/icomoon.eot?z1lat9');
src: url('fonts/icomoon.eot?z1lat9#iefix') format('embedded-opentype'),
url('fonts/icomoon.ttf?z1lat9') format('truetype'),
url('fonts/icomoon.woff?z1lat9') format('woff'),
url('fonts/icomoon.svg?z1lat9#icomoon') format('svg');
font-weight: normal;
font-style: normal;
}
[class^="icon-"], [class*=" icon-"] {
/* use !important to prevent issues with browser extensions that change fonts */
font-family: 'icomoon' !important;
speak: none;
font-style: normal;
font-weight: normal;
font-variant: normal;
text-transform: none;
line-height: 1;
/* Better Font Rendering =========== */
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
@media (max-width: 992px) {
.clear-margin-sm {
margin-bottom: 0;
}
}
.fs-lg {
font-size: 1.33333333em;
line-height: 0.75em;
vertical-align: -15%;
margin-right: 17px;
}
.fs-2x {
font-size: 2em;
}
.fs-3x {
font-size: 3em;
}
.fs-4x {
font-size: 4em;
}
.btn-circle-sm {
width: 28px;
height: 28px;
line-height: 28px;
border-radius: 50%;
text-align: center;
padding: 0;
outline: none !important;
}
/* the version when the icon file is last updated */
@font-face {
font-family: 'jst-elegant';
src: url("fonts/icomoon.eot?f218wo");
src: url("fonts/icomoon.eot?f218wo#iefix") format('embedded-opentype'), url("fonts/icomoon.ttf?f218wo") format('truetype'), url("fonts/icomoon.woff?f218wo") format('woff'), url("fonts/icomoon.svg?f218wo#icomoon") format('svg');
font-weight: normal;
font-style: normal;
}
[class^="icon-"],
[class*=" icon-"] {
/* use !important to prevent issues with browser extensions that change fonts */
font-family: 'jst-elegant' !important;
}
.mr-5 {
margin-right: 5px;
}
/* Get the Unicode characters from here https://fontawesome.com/cheatsheet/free/regular */
.icon-board:before {
content: "\e608";
}
.icon-office:before {
content: "\e606";
}
.icon-tools:before {
content: "\e034";
}
.icon-graduation-cap:before {
content: "\f19d";
}
.icon-heart:before {
content: "\f004";
}
.icon-language:before {
content: "\f1ab";
}
.icon-github:before {
content: "\f09b";
}
.icon-link:before {
content: "\f0c1";
}
.icon-linkedin:before {
content: "\f08c";
}
.icon-clock:before {
content: "\e60a";
}
</style>
</head>
<body itemscope itemtype="https://schema.org/Person">
<div class="container">
<section class="row">
<div class="card-wrapper">
<div class="card">
<div class="profile-card">
<div class="profile-pic">
<img class="media-object img-circle center-block" data-src="holder.js/100x100"
alt="Haseeb Tariq"
style="cursor: pointer !important;"
src="profile.jpg"
itemprop="image" onclick="window.open('/', '_self');">
</div>
<div class="contact-details">
<div class="name-and-profession">
<h3 class="text-bolder name" itemprop="name"
style="cursor: pointer !important;"
onclick="window.open('/', '_self');">Haseeb Tariq</h3>
<h5 class="text-muted" itemprop="jobTitle">Data Scientist</h5>
</div>
<div class="detail">
<span class="icon">
<i class="fa fa-lg fa-envelope"></i>
</span>
<span class="info">
<a href="mailto:[email protected]" class="link-disguise" itemprop="email">[email protected]</a>
</span>
</div>
<div class="detail">
<span class="icon">
<i class="fa fa-lg fa-phone"></i>
</span>
<a href="https://www.linkedin.com/in/mhaseebtariq"
target="_blank" class="link-disguise info" itemprop="telephone">
[Request on LinkedIn]
</a>
</div>
<div class="detail">
<span class="icon">
<i class="fa fa-lg fa-map-marker"></i>
</span>
<span class="info" style="margin-left: 4px;">
Utrecht, The Netherlands
</span>
</div>
</div>
</div>
<hr>
<div class="social-links">
<a class="social-link" href="https://scholar.google.com/citations?user=XJDrx_EAAAAJ&hl=en" target="_blank">
<span class="tippy fa-brands fa-google-scholar fa-3x" data-tippy-content="Google Scholar"></span>
</a>
<a class="social-link"
href="https://github.com/mhaseebtariq" target="_blank">
<span class="tippy fa-brands fa-github fa-3x" data-tippy-content="Github"></span>
</a>
<a class="social-link"
href="https://www.linkedin.com/in/mhaseebtariq" target="_blank">
<span class="tippy fa-brands fa-linkedin fa-3x" data-tippy-content="LinkedIn"></span>
</a>
<a class="social-link" href="https://medium.com/@mhaseebtariq/" target="_blank">
<span class="tippy fa-brands fa-medium fa-3x" data-tippy-content="Blogs"></span>
</a>
<a class="social-link" href="offline.html" target="_blank">
<span class="tippy fa-solid fa-print fa-3x" data-tippy-content="Print Version"></span>
</a>
<a class="social-link" href="CV-MHT.pdf">
<span class="tippy fa-solid fa-file-pdf fa-3x" data-tippy-content="Download PDF"></span>
</a>
</div>
</div>
</div>
</section>
<section class="row">
<div class="card-wrapper">
<div class="card" style="border: none !important; padding: none !important; padding: 0.5em 3.5em !important;">
<a onclick="goBack()" role="button" style="cursor: pointer;">
<h4 id="back-page"><span class="fa fa-backward"></span><span class="title t-details">Back</span></h4>
</a>
</div>
</div>
<div class="card-wrapper">
<div class="card background-card">
<h4 id="challenges"><span class="fa fa-flag-checkered"></span><span class="title t-details">Challenges</span> </h4>
<div class="card-nested" itemprop="description">
<p>
It's a very unique demand forecasting problem. Following are some of the aspects of the problem:
<ul>
<li>BrandLoyalty runs loyalty programs in several different regions of the world</li>
<li>Each country/region has different drivers for the demand</li>
<li>The demand drivers could be based on a variety of things:
<ul>
<li>Socioeconomic factors</li>
<li>The cultural dimensions</li>
<li>Retailer's type, size, and reach</li>
<li>Weather</li>
<li>… and more</li>
</ul>
</li>
<li>A huge number of different program parameters</li>
<li>Every (important) combination of predictors has a very few data points</li>
<li>Even the entire data set is not big enough for any model to learn much</li>
<li>Some of the past programs had noisy data because of unforeseen events</li>
</ul>
</p>
</div>
<h4 id="solution"><span class="fa fa-exclamation-circle"></span><span class="title t-details">Solution</span> </h4>
<div class="card-nested" itemprop="description">
<p>
Because of the complexity of the problem, we had to start with a complex (/flexible) model. Realising that for a small
percentage of programs we have noisy data, we started with "random forest". Even though the accuracy was better than a
simple linear or a tree (based) model, the predictions were not very stable.
<br><br>
We then moved on to a "Boosting" based model, Xgboost. The accuracy on the train set was consistently higher than the
random forest model. On the other hand, it was overfitting a lot — the accuracy on the the unseen data was very low.
In the subsequent iterations we tried "early stopping", by separating a small set of "evaluation set" from the train set.
This resulted in the accuracy of the model tightly tied to the quality and relevance of the evaluation set. There also didn't seem
to be a "consistently" smart way of selecting the evaluation set.
<br><br>
In the next iteration we repeated the training of the Xgboost model by randomly sampling (with replacement) the evaluation sets.
For the most recent version, we trained the model 10,000 times. By aggregating (mean) the predictions we got consistently stable results.
The overall accuracy was also much higher then all the previous versions. An added advantage of this method is that, now we can
also report the predictions intervals by calculating the 5th and 95th percentiles of the 10,000 predictions.
<br><br>
The actual implementation is much more complex than this explanation. This description only provides the overview of some of
the interesting design decisions.
</p>
</div>
</div>
</div>
</section>
</div>
</body>
</html>