-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
761 lines (674 loc) · 37 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
<!DOCTYPE html>
<html lang="en" prefix="og: http://ogp.me/ns#">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<!-- Document Title -->
<!-- Ideally, include primary keyword -->
<title>Robert Rusev's Data Science World</title>
<!-- Meta Description -->
<!-- Include primary keywords and make it compelling -->
<meta name="description" content="Dive into Robert Rusev's world of data science and machine learning, where insightful analysis transforms into lucrative opportunities and innovative solutions.">
<!-- Meta Keywords (Consider removing or reducing the number of keywords) -->
<meta name="keywords" content="Data Science, Machine Learning, Data Visualization, Python, Deep Learning, Statistical Analysis, NLP">
<!-- Author -->
<meta name="author" content="Robert Rusev">
<!-- Favicon -->
<link rel="icon" href="./RR_site_files/favicon.ico">
<!-- Open Graph Tags -->
<meta property="og:title" content="Robert Rusev's Data Science World">
<meta property="og:image" content="https://www.robertrusev.github.io/RR_site_files/social_media_share_photo_1200_630.png">
<meta property="og:description" content="Welcome to my data science world! Where insights transform into opportunities. Embracing new horizons.">
<meta property="og:url" content="https://www.robertrusev.github.io">
<meta property="og:type" content="website">
<!-- Canonical URL -->
<link rel="canonical" href="https://www.robertrusev.github.io"/>
<!-- Google Site Verification -->
<meta name="google-site-verification" content="JHe9CCUMqTAxqzGW-LzdaoHZqjPmpHKUVO29TF0qrN8" />
<!-- Bootstrap core CSS -->
<link href="./RR_site_files/bootstrap.min.css" rel="stylesheet">
<!-- Custom fonts for this template -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
<link href="./RR_site_files/css" rel="stylesheet" type="text/css">
<link href="./RR_site_files/css(1)" rel="stylesheet" type="text/css">
<link href="./RR_site_files/css(2)" rel="stylesheet" type="text/css">
<link href="./RR_site_files/css(3)" rel="stylesheet" type="text/css">
<!-- Custom styles for this template -->
<link href="./RR_site_files/agency.css" rel="stylesheet">
<!-- Leaflet -->
<link rel="stylesheet" href="./RR_site_files/leaflet.css" integrity="sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZMZ19scR4PsZChSR7A==" crossorigin="">
<script src="./RR_site_files/leaflet.js" integrity="sha512-XQoYMqMTK8LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA==" crossorigin=""></script>
<!-- Structured Data -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Person",
"name": "Robert Rusev",
"url": "https://www.robertrusev.github.io",
"image": "https://www.robertrusev.github.io/RR_site_files/social_media_share_photo_1200_630.png",
"sameAs": [
"https://www.linkedin.com/in/robertrusev"
],
"jobTitle": "Data Scientist",
"worksFor": {
"@type": "Organization",
"name": "Self-Employed"
}
}
</script>
<!-- Inline CSS -->
<style>
.privacy-modal-dialog {
max-width: 360px;
margin: 0 auto;
}
@media (max-width: 767px) {
.navbar-toggler-icon {
display: none;
}
}
.masthead {
min-height: 100vh; /* This ensures that the masthead is at least the height of the viewport */
position: relative; /* This can help contain absolutely positioned children if needed */
}
</style>
</head>
<body id="page-top">
<!-- Navigation -->
<nav class="navbar navbar-expand-lg fixed-top" id="mainNav" aria-label="Main navigation">
<div class="container">
<a class="js-scroll-trigger" href="#page-top">HOME</a> <!-- Kept your original HOME button style -->
<button class="navbar-toggler navbar-toggler-right" type="button" data-toggle="collapse" data-target="#navbarResponsive" aria-controls="navbarResponsive" aria-expanded="false" aria-label="Toggle navigation menu">
Menu
</button>
<div class="collapse navbar-collapse" id="navbarResponsive">
<ul class="navbar-nav text-uppercase ml-auto">
<li class="nav-item">
<a class="nav-link js-scroll-trigger" href="#about-me" aria-label="About me section">About me</a>
</li>
<li class="nav-item">
<a class="nav-link js-scroll-trigger" href="#portfolio" aria-label="View portfolio section">Portfolio</a>
</li>
<li class="nav-item">
<a class="nav-link js-scroll-trigger" href="#contact" aria-label="Contact or ask me section">Ask me</a>
</li>
<!-- Other navigation items... -->
</ul>
</div>
</div>
</nav>
<!-- ======================= LANDING SECTION ======================= -->
<header class="masthead">
<div id="particles-js">
<canvas class="particles-js-canvas-el" style="width: 100%; height: 100%;"></canvas>
</div>
<div class="textlanding">
<p style="display:inline; font-size: 35px; margin-left: -15px;">ROBERT</p> <!-- Adjust margin-left value as needed -->
<img width="90px" style="display:inline; margin-bottom: 20px; margin-right: 10px; margin-left: 10px;" src="./RR_site_files/landing_page_logo.png" alt="Robert Rusev Logo"> <!-- Adjust margin-left value as needed -->
<p style="display:inline; font-size: 35px;">RUSEV</p>
<hr style="width: 100px; height: 1px; border:none;color:#333;background-color:#333;">
<br>
<ul class="list-inline social-buttons">
<li class="list-inline-item">
<a href="https://github.com/RobertRusev" title="Visit Robert's GitHub profile">
<i class="fab fa-github" style="color: white"></i>
</a>
</li>
<li class="list-inline-item">
<a href="https://www.linkedin.com/in/robert-rusev-bb50b8131/" title="Visit Robert's LinkedIn profile">
<i class="fab fa-linkedin"></i>
</a>
</li>
</ul>
<br><br>
<p><strong>Welcome to my data science world! </strong><br>Where insights transform into opportunities. Embracing new horizons.</p>
<a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger" href="#contact" aria-label="Contact Me">Contact</a>
</div>
<div class="arrowlanding">
<a class="js-scroll-trigger" href="#about-me" aria-label="Scroll to About Me section">
<p style="font-size: 80px; color: #69b3a2">﹀</p>
</a>
</div>
</header>
<script src="./RR_site_files/particles.js"></script>
<script src="./RR_site_files/appHome.js"></script>
<!-- ======================= ABOUT ME SECTION ======================= -->
<section id="about-me" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-4 text-center">
<img class="img-fluid" src="./RR_site_files/RR_photo.png" alt="Yan Holtz profile picture" style="margin-bottom: 30px">
</div>
<div class="col-lg-8 text-center text-md-left">
<p>Hi, I'm Robert and welcome to my data science world!</p>
<p>I'm an aspiring data scientist with a BA in Economics from the University of Manchester.
<br>My academic journey laid the foundation for my analytical skills, particularly in econometrics, advanced mathematics,
and statistics, which are indispensable for data-driven decision-making.</p>
<p>My professional journey began at BNY Mellon as an Operations Analyst in their Manchester office, where I excelled in roles related to
process improvement and financial operations.
Beyond that, I've dedicated myself to continuous learning. I've completed courses at Software University, focusing on data science like :
<li>Data science</li>
<li>Advanced Python</li>
<li>Python OOP</li>
<li>Fundamentals with Python</li>
</p>
<p>
I am a native Bulgarian speaker, bilingual in English, and proficient in both Russian and Italian.
My journey in the world of data science is a testament to my passion for extracting valuable insights from data.
I'm eager to leverage my educational background, professional experiences, and technical skills to tackle new challenges and
make a meaningful impact in the data science field.
<br>Have a look at my portfolio!</p>
<a class="btn btn-secondary btn-l text-uppercase js-scroll-trigger" href="#portfolio">Portfolio</a>
<a class="btn btn-secondary btn-l text-uppercase" href="https://www.linkedin.com/in/robert-rusev-bb50b8131/">LinkedIn</a>
<a class="btn btn-secondary btn-l text-uppercase" href="https://github.com/RobertRusev">Github</a>
</div>
</div>
<div class="row hide-if-small-screen" style="padding-top: 5%">
<div class="col-lg-12 text-center">
<svg width="863" height="100">
<line x1="0" y1="50" x2="850" y2="50" style="stroke:black; stroke-width:1.5"></line>
<circle cx="220" cy="50" r="10" stroke="#f8f9fa" stroke-width="6" fill="#69b3a2"></circle>
<text text-anchor="middle" x="220" y="80" fill="black">2018</text>
<text font-size="13" text-anchor="middle" x="70" y="30" fill="black">Economics graduate</text>
<text style="font-style: italic" font-size="13" text-anchor="middle" x="90" y="80" fill="#808080">University of Manchester</text>
<circle cx="450" cy="50" r="10" stroke="#f8f9fa" stroke-width="6" fill="#69b3a2"></circle>
<text text-anchor="middle" x="450" y="80" fill="black">2019</text>
<text font-size="13" text-anchor="middle" x="320" y="30" fill="black">Operations analyst</text>
<text style="font-style: italic" font-size="13" text-anchor="middle" x="320" y="80" fill="#808080">BNY Mellon</text>
<circle cx="650" cy="50" r="10" stroke="#f8f9fa" stroke-width="6" fill="#69b3a2"></circle>
<text text-anchor="middle" x="650" y="80" fill="black">2023</text>
<text font-size="13" text-anchor="middle" x="550" y="30" fill="black">Middle office analyst</text>
<text style="font-style: italic" font-size="13" text-anchor="middle" x="550" y="80" fill="#808080">BNY Mellon</text>
<text font-size="13" text-anchor="middle" x="750" y="30" fill="black">Data scientist</text>
<text style="font-style: italic" font-size="13" text-anchor="middle" x="750" y="80" fill="#808080">Exploring new horizons</text>
</svg>
</div>
</div>
</div>
</section>
<!-- ======================================================================= -->
<!-- ======================== PORTFOLIO SECTION ============================ -->
<style>
.explanation_portfolio {
font-size: 14px;
margin: 20px;
}
</style>
<section class="bg-light" id="portfolio">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Portfolio</h2>
<h3 class="section-subheading text-muted">The projects I've been working on</h3>
<div id="portfolio-button-container">
<button class="btn btn-secondary active" data-portfolio-section="all">Show all</button>
<button class="btn btn-secondary" data-portfolio-section="classification">Classification</button>
<button class="btn btn-secondary" data-portfolio-section="regression">Regression</button>
<button class="btn btn-secondary" data-portfolio-section="nlp">Natural Language Processing</button>
</div>
<br>
</div>
</div>
<br><br>
<div id="portfolio-items" class="row">
<div class="col-md-3 col-sm-6 portfolio-item show column dataviz regression dashboard shiny">
<a class="portfolio-link" data-toggle="modal" href="#StockPricePredictor">
<div class="portfolio-hover">
<div class="portfolio-hover-content">
<p>Stock price predictor</p>
<hr>
<p class="explanation_portfolio"> Leveraging historical data, macroeconomic indicators, LSTM and Prophet models for enhanced stock price forecasting.<br></p>
</div>
</div>
<img class="img-fluid" src="./RR_site_files/StockPricePredictor.png" alt="">
</a>
</div>
<div class="col-md-3 col-sm-6 portfolio-item show column dataviz classification dashboard shiny">
<a class="portfolio-link" data-toggle="modal" href="#FinFraudDetector">
<div class="portfolio-hover">
<div class="portfolio-hover-content">
<p>Financial Fraud Detector</p>
<hr>
<p class="explanation_portfolio">Utilising XGBoost, precision-recall, and ROC curves to detect financial transaction fraud.</p>
</div>
</div>
<img class="img-fluid" src="./RR_site_files/FinFraudDetector.png" alt="">
</a>
</div>
<div class="col-md-3 col-sm-6 portfolio-item show column nlp dashboard shiny">
<a class="portfolio-link" data-toggle="modal" href="#FinHeadlines-MoodTracker">
<div class="portfolio-hover">
<div class="portfolio-hover-content">
<p>Financial headlines <br> Mood Tracker</p>
<hr>
<p class="explanation_portfolio">Analysing the sentiment of financial headlines using LSTM and some CNN methods.</p>
</div>
</div>
<img class="img-fluid" src="./RR_site_files/FinHeadlinesMoodTracker.png" alt="">
</a>
</div>
<div class="col-md-3 col-sm-6 portfolio-item show column dataviz classification shiny">
<a class="portfolio-link" data-toggle="modal" href="#Loan-Default-Predictor">
<div class="portfolio-hover">
<div class="portfolio-hover-content">
<p>Loan Default Predictor</p>
<hr>
<p class="explanation_portfolio">Making informed lending decisions through comparing three models - Logistic Regression, Random Forest and XGBoost.</p>
</div>
</div>
<img class="img-fluid" src="./RR_site_files/LoanDefaultPredictor.png" alt="">
</a>
</div>
<div class="col-md-3 col-sm-6 portfolio-item show column regression mining">
<a class="portfolio-link" data-toggle="modal" href="#PL-Wins-Predictor">
<div class="portfolio-hover">
<div class="portfolio-hover-content">
<p>Premier League Wins <br>Predictor</p>
<hr>
<p class="explanation_portfolio">My first project, aiming to predict PL wins via linear regression.<br></p>
</div>
</div>
<img class="img-fluid" src="./RR_site_files/PremierLeagueWinsPredictor.png" alt="">
</a>
</div>
</div>
</div>
</section>
<!-- Stock Price predictor -->
<center>
<div class="portfolio-modal modal fade" id="StockPricePredictor" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100">
<div class="modal-content"style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<img src="./RR_site_files/StockPricePredictor.png" width="300px">
<h4>Predicting times series stock prices.</h4>
<p style="max-width: 600px">Testing the performance of LSTM and Prophet on the unpredictability of the stock market.</p>
<div style="text-align: left">
<h6>Description</h6>
<hr>
<p>Leveraging historical data, macroeconomic indicators, LSTM and Prophet models for enhanced stock price forecasting.
Analyze trends, patterns, and economic factors to gain insights and make data-driven predictions.
Leverage advanced modeling techniques for reliable forecasts.</p>
<h6>Features</h6>
<hr>
<ul>
<li>Historical Data Analysis: Gain insights from historical stock price data to identify trends, patterns, and correlations.</li>
<li>Macroeconomic Indicators: Incorporate macroeconomic indicators to enhance the predictive capabilities of the models.</li>
<li>LSTM Model: Utilize the LSTM model to capture long-term dependencies in stock price data and make accurate predictions.</li>
<li>Prophet Model: Leverage the Prophet model for time series forecasting, taking into account seasonality and trends.</li>
<li>Evaluation Metrics: Evaluate the performance of the models using metrics such as mean squared error (MSE) and mean absolute error (MAE).</li>
</ul>
<h6>Dataset</h6>
<hr>
<p>The project uses historical stock price data of Apple Inc. (AAPL) as an example, but it can be easily adapted to any other stock.
The dataset includes daily stock prices, volume, and other relevant attributes.
You can replace the dataset with your desired stock data to perform analysis and predictions.</p>
<h6>Read more</h6>
<hr>
<span>The jupyter notebook which features explanations is available on GitHub.</span>
<br><br>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/ML-TimeSeries-StockPricePredictor">GitHub</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div></center>
<!-- FinFraudDetector -->
<center>
<div class="portfolio-modal modal fade" id="FinFraudDetector" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100">
<div class="modal-content"style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<img src="./RR_site_files/FinFraudDetector.png" width="300px">
<h4>Finding fraudulent transactions.</h4>
<p style="max-width: 600px">FinFraud-Detector is a machine learning project for detecting financial transaction fraud.
Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection.
Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.</p>
<div style="text-align: left">
<h6>Introduction</h6>
<hr>
<p>Financial transaction fraud poses a significant threat to organizations and individuals.
ML-FinFraud-Detector offers an effective solution to identify fraudulent activities, helping enhance financial security.
By leveraging machine learning techniques, this project can analyze transaction data and classify transactions as either fraudulent or legitimate.
</p>
<h6>Features</h6>
<hr>
<ul>
<li>Utilizes the XGBoost algorithm for robust fraud detection</li>
<li>Incorporates precision-recall and ROC curves for performance evaluation</li>
<li>Feature importance analysis to identify influential factors</li>
<li>Helps organizations prevent financial losses and enhance security</li>
</ul>
<h6>Dataset</h6>
<hr>
<p>The Bank Account Fraud (BAF) suite of datasets, recently published at NeurIPS 2022, offers a comprehensive collection of
synthetic bank account fraud tabular datasets.
This suite comprises six distinct datasets, making it a valuable resource for evaluating both novel and existing machine learning (ML)
and fair ML methods in the field of fraud detection.</p>
<h6>Read more</h6>
<hr>
<span>The jupyter notebook which features explanations is available on GitHub.</span>
<br><br>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/ML-FinFraud-Detector">GitHub</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div></center>
<!-- FinHeadlines-MoodTracker -->
<center>
<div class="portfolio-modal modal fade" id="FinHeadlines-MoodTracker" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100">
<div class="modal-content"style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<img src="./RR_site_files/FinHeadlinesMoodTracker.png" width="300px">
<h4>Predicting the sentiment of financial news headlines.</h4>
<p style="max-width: 600px">Leveraging LSTM and some CNN methods to classify the sentiment.</p>
<div style="text-align: left">
<h6>Description</h6>
<hr>
<p>FinHeadlines-MoodTracker is an NLP project that performs sentiment analysis on financial news headlines.
It aims to predict the sentiment (positive, negative, or neutral) associated with the news headlines and track the overall mood in the financial market.
The project utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for sentiment classification.</p>
<h6>Features</h6>
<hr>
<p>The project includes the following components:</p>
<ul>
<li>Data preprocessing: The financial news headlines are preprocessed to remove noise, tokenize the text, and perform other necessary cleaning steps.</li>
<li>Word embedding: The project uses pre-trained word embeddings to represent words as dense vectors and capture semantic relationships.</li>
<li>Sentiment analysis model: The model consists of an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.</li>
<li>Training and evaluation: The model is trained on a labeled dataset of financial news headlines and evaluated using appropriate metrics.</li>
<li>Mood tracking: The model's predictions are used to track the overall sentiment or mood in the financial market.</li>
</ul>
<h6>Dataset</h6>
<hr>
<p>The dataset was originally introduced and described in a research paper titled
"Good debt or bad debt: Detecting semantic orientations in economic texts" by Malo, P., Sinha, A., Takala, P., Korhonen, P., and Wallenius, J. (2014).
The paper was published in the Journal of the American Society for Information Science and Technology.</p>
<h6>Read more</h6>
<hr>
<span>The jupyter notebook which features explanations is available on GitHub.</span>
<br><br>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/NLP-FinHeadlines-MoodTracker">GitHub</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div></center>
<!-- Loan-Default-Predictor -->
<center>
<div class="portfolio-modal modal fade" id="Loan-Default-Predictor" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100">
<div class="modal-content"style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<img src="./RR_site_files/LoanDefaultPredictor.png" width="300px">
<h4>Making informed lending decisions.</h4>
<p style="max-width: 600px">Working with and comparing three models - Logistic Regression, Random Forest and XGBoost .</p>
<div style="text-align: left">
<h6>Description</h6>
<hr>
<p>Loan Default Predictor is a machine learning project that aims to predict loan defaults using various algorithms such as Logistic Regression, Random Forest, and XGBoost.
The project includes data preprocessing, model training, feature analysis, and making informed lending decisions based on the predictions.</p>
<h6>Features</h6>
<hr>
<p> You can find more information in this article.</p>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/ML-Loan-Default-Predictor/blob/main/ML-Loan-Default-Predictor-article.txt">Article</a>
<br>
<h6>Dataset</h6>
<hr>
<p>The project utilizes a dataset containing loan information such as borrower details, loan amount, interest rate, credit score, employment history, etc.
The data is provided in a CSV format and should be placed in the data directory of this repository.</p>
<h6>Read more</h6>
<hr>
<span>The jupyter notebook which features explanations is available on GitHub.</span>
<br><br>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/ML-Loan-Default-Predictor">GitHub</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div></center>
<!-- PL-Wins-Predictor -->
<center>
<div class="portfolio-modal modal fade" id="PL-Wins-Predictor" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100">
<div class="modal-content"style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<img src="./RR_site_files/PremierLeagueWinsPredictor.png" width="300px">
<h4>Aiming to predict PL wins.</h4>
<p style="max-width: 600px">First project, using a simple Linear Regression model to predict PL wins based on historical occurrences.</p>
<div style="text-align: left">
<h6>Description</h6>
<hr>
<p>The main goal of this project is to build a linear regression model to predict the number of wins for each team in a Premier League season.
Additionally, we want to identify the key factors that contribute to a team becoming a champion.
By doing so, we aim to gain insights into what makes a team successful in the Premier League.</p>
<h6>Features</h6>
<hr>
<ul>
<li>Data Acquisition and Exploration: Reading and exploring the datasets to understand the available data.</li>
<li>Data Preprocessing: Cleaning and preparing the data for further analysis.</li>
<li>Exploratory Data Analysis (EDA): Visualizing and analyzing the data to gain insights into the trends and patterns.</li>
<li>Model Building: Constructing a linear regression model to predict the number of wins and identifying important features.</li>
<li>Conclusion: Summarizing the findings and key takeaways from the analysis.</li>
</ul>
<h6>Dataset</h6>
<hr>
<p>The project utilizes several datasets related to the Premier League:</p>
<ul>
<li>results.csv: Contains information about all the match results in the Premier League between the 2006/07 season and the 2017/18 season.</li>
<li>stats.csv: Expands on the statistical categories that are official for the Premier League and will be used for analysis.</li>
<li>EPL standings 2000-2022.csv: Provides the standings of the individual teams throughout the seasons 2000-2022.</li>
<li>with_goalscorers.csv: Provides the top goalscorers from the formation of the Premier League in 1992 until now.</li>
</ul>
<h6>Read more</h6>
<hr>
<span>The jupyter notebook which features explanations is available on GitHub.</span>
<br><br>
<a class="btn btn-secondary btn text-uppercase js-scroll-trigger mx-auto d-block"
href="https://github.com/RobertRusev/ML-Premier-League-Wins-Predictor">GitHub</a>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div></center>
<!-- ======================= CONTACT SECTION =============================== -->
<section id="contact" class="bg-light">
<div class="container">
<div class="row">
<div class="col-lg-2 text-center"></div>
<div class="col-lg-8 text-center">
<br><br><br>
<h2 class="section-heading text-uppercase" style="color: black">Contact</h2>
<p>You made it thus far. Great! You can contact me about anything via these links.<br><br>
<a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger" href="https://www.linkedin.com/in/robert-rusev-bb50b8131/">LinkedIn</a>
<a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger" href="https://github.com/RobertRusev">Github</a>
<a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger" href="mailto:robert.rusev@yahoo.com">Mail</a>
</div>
</div>
</div>
</div>
</section>
<!-- ======================= FOOTER SECTION ================================ -->
<footer>
<div class="container">
<div class="row">
<div class="col-md-4">
<span class="copyright">Copyright © Rusev Robert 2023</span>
</div>
<div class="col-md-4">
<ul class="list-inline social-buttons">
<li class="list-inline-item">
<a href="https://github.com/RobertRusev">
<i class="fab fa-github"></i>
</a>
</li>
<li class="list-inline-item">
<a href="https://www.linkedin.com/in/robert-rusev-bb50b8131/">
<i class="fab fa-linkedin"></i>
</a>
</li>
</ul>
</div>
<div class="col-md-4">
<ul class="list-inline quicklinks">
<li class="list-inline-item">
<a href="#" onclick="openPrivacyModal()">Privacy Policy</a>
</li>
</ul>
</div>
</div>
</div>
</footer>
<!-- ======================= PRIVACY POLICY SECTION ================================ -->
<div id="privacyModal" class="modal" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog h-100 privacy-modal-dialog">
<div class="modal-content" style="max-height: 80vh; overflow-y: scroll;">
<div class="close-modal" data-dismiss="modal">
<div class="lr">
<div class="rl"></div>
</div>
</div>
<div class="container">
<div class="row">
<div class="col-lg-10 mx-auto">
<div class="modal-body">
<span class="close" onclick="closePrivacyModal()">×</span>
<h2>Privacy Policy</h2>
<p><strong>Last Updated:</strong> 14th of Sept 2023</p>
<p>Robert Rusev ("us," "we," or "our") operates robertrusev.github.io (the "Site").
This page informs you that we do not collect any personal information or data from users of the Site.</p>
<p><strong>No Data Collection</strong> <br>
We do not collect any personally identifiable information or data from visitors to our website.</p>
<p><strong>Contact Us </strong><br>
If you have any questions about our privacy policy or the use of this website, please feel free to contact us
<a href="mailto:robert.rusev@yahoo.com">here</a>.</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- JavaScript to control the modal -->
<script>
var privacyModal = document.getElementById("privacyModal");
function openPrivacyModal() {
privacyModal.style.display = "block";
}
function closePrivacyModal() {
privacyModal.style.display = "none";
}
// Add an event listener to close the modal when clicking outside of it
window.addEventListener("click", function(event) {
if (event.target === privacyModal) {
closePrivacyModal();
}
});
</script>
<!-- JavaScript for smooth scrolling -->
<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
<script>
$(document).ready(function () {
$("a.js-scroll-trigger").on("click", function (event) {
if (this.hash !== "") {
event.preventDefault();
const hash = this.hash;
$("html, body").animate(
{
scrollTop: $(hash).offset().top,
},
800, // Animation duration in milliseconds
function () {
window.location.hash = hash;
}
);
}
});
});
</script>
<!-- Bootstrap core JavaScript -->
<script src="./RR_site_files/jquery.min.js"></script>
<script src="./RR_site_files/bootstrap.bundle.min.js"></script>
<!-- Plugin JavaScript -->
<script src="./RR_site_files/jquery.easing.min.js"></script>
<!-- Custom scripts for this template -->
<script src="./RR_site_files/agency.min.js"></script>
<script src="./RR_site_files/portfolio.js"></script>
<!-- Load d3.js -->
<script src="./RR_site_files/d3.v4.min.js"></script>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async="" src="./RR_site_files/js(2)"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() { dataLayer.push(arguments); }
gtag('js', new Date());
gtag('config', 'UA-79254642-3');
</script>
</body>
</html>