Zoya Ali

Academic

Verbatim text

Zoya Ali
+923311333244, zoyaa5373@gmail.com
House.2093, Street# 69, 1-10/1, Islamabad
Linkedln: http://linkedin.com/in/zoya-ali-242479279
Bachelor of Business Analytics (BSBA)
Major:
Business Analytics
Roots International School, Islamabad
- Levels (Commerce/Business)
Roots International School, Islamabad
- Levels (Computer Science)
rojects
inal Pro ·ect: Data-Driven Approach to Visual Search: Enhancing Customer Experience in Apparel Industry
hrough Machine Learning.
ransforming apparel shopping with machine learning: A data-driven study on visual search, user experience, and
purchase behavior.
Semester Projects
Marketing Analytic Plan for SHAN Shoop Noodles -(Marketing Management)
Marketing Analytic Plan for I MTIAZ - (Empowering Organizations with Analytics and Data Visualization)
upermarket Sales Prediction - Machine learning for Business Analytics
Research paper: Decoding success: How Business Intelligence Shapes Employee Behavior and Drives Decision
Excellence - (Method in Business Research)
ales and Inventory Management System for a Retail Superstore with ER Diagram and SQL Implementation -
(Database system for Business)
Report on analysis of the performance of five global stocks using SPSS, Excel, and Eviews - (Business Econometrics)
Work Experience
Gemnine private Limited, Islamabad.
June 2020- Sep 2020
Intern at Fintelli solutions, Lahore.
Jul 2022 - Nov 2022
Intern at Roots International School, Islamabad.
June 2023- Nov 2023
Market Research Analytics (Freelance), Remote.
June 2024- Nov 2024
kills & Tools
Project management, Public relations, Teamwork, Time management, Problem solving,
nalytical skills, Effective communication skills, research skills.
echnical Skills
anva, SQL, Html, SPSS, Python, Eviews, QuickBooks, Rstudio, PowerBI, MS-Office, MS-
ord, MS-PowerPoint, MS Excel, MS-Visio, tableau.
chievements
ice President -Roots Youth Model United Nations 2016
HR head for Recruiting teams - Roots Youth Model United Nations 2017
Business Case Simulation team member - NASCON 2022
earn Decor - NASCON 2023
Trainings/ Certification
Microsoft Power Bl data analyst Associate, Excel for business and Finance (Coursera)
ctivities
Organizer of Welcome'16 at Roots International School, Organizer of convocation event'17 at Roots international
chool, Organizer of Seminar at FAST.
nterests
Reading, Writing ,Adventure, Calligraphy.
FAST NUCES ISLAMABAD CAMPUS
FAST SCHOOL OF
MANAGEMENT
FINAL YEAR PROJECTS
FAST NUCES ISLAMABAD CAMPUS
Default Risk Prediction: An ML-Based Estimation For Listed Companies
in Asia.
Our topic focuses on using machine learning algorithms to predict when Asian companies might fail
to meet their financial obligations. This project is important because accurate predictions can help
investors and regulators reduce the financial losses associated with company failures. By
concentrating on Asian markets, we address the specific economic and regulatory factors that affect
these regions. This project is necessary as it provides valuable tools to manage financial risks better
and supports stable economic growth in Asia.
Key Words: Default Risk Prediction, Machine Learning, Predictive Modelling.
Area of Study:
Finance
Supervisor Name:
Dr. Bilal Saeed
Group Members:
Shizza Kiani (i21 - 1416)
Muneeb Ahmed (i21 - 1438)
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Ibrahim Mahmood (i21 - 1442)
FAST NUCES ISLAMABAD CAMPUS
Predicting Banks' credit risk by using machine learning and deep
learning approaches: A comparative study of Islamic and conventional
banks listed in Asia.
The project's goal is to create a predictive framework for evaluating credit risk in banks using deep
learning and advanced machine learning techniques. Accurately assessing credit risk is becoming
increasingly difficult for the banking industry, especially in Asia, because of changing market
conditions and laws. To show the distinctions between Islamic and conventional banks, this study
will evaluate risk factors and model performance. The research aims to give a precise, data-driven
approach to credit risk prediction by utilizing a variety of machine learning and deep learning
approaches. This will ultimately improve risk management practices while improving financial
stability.
Key Words: Credit Risk Prediction, Machine Learning, Predictive Modelling, Islamic Banks, Conventional
Banks.
Supervisor: Dr. Bilal Saeed
Group Members:
Komail Jawaid (21i-1434)
Menahil Pervaiz (211-1425)
Saad Rehman
(21i-1448)
Zainab Siddiqui (211-2604)
A comparative study of Islamic and Conventional
banks listed in Asia.
Objectives/Goals
Motivation
• Develop a predictive model for credit
• Growing need for At-driven risk
risk assessment
assessment in banking.
• Compare Islamic and Conventional
• Limited research on Islamic vs.
banks' risk profiles.
Conventional banks in Asia.
• Implement Machine Leaming (ML)
and Deep Leaming (DL) models.
• Increasing use of MUDL for financial
risk prediction.
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Area of Study:
Finance
Supervisor Name:
Dr. Bilal Saeed
Group Members:
Komail Jawaid (21i-1434)
Menahil Pervaiz (21i-1425)
Saad Rehman (21i-1448)
Zainab Siddiqui (21i-2604)
FAST NUCES ISLAMABAD CAMPUS
Economic Growth: A Machine Learning Approach
One of the most important measures of a country's economic health is its economic growth
measured by gross domestic product (GDP) growth rate serving as a comprehensive
measure of a country's overall economic output and performance. Accurately forecasting
GDP growth rate is essential for informed economic policy-making, budget planning, and
financial decision-making. Previously regression models have been used to forecast GDP
growth, but more advanced machine learning (ML) techniques offer more effective means
of managing intricate time series data and enhancing prediction precision.
Key Words: (Add 5 Key words i.e technologies/themes reflecting your study)
Area of Study:
Finance
Supervisor Name:
Mr. Mansoor Mushtaq
Group Members:
Mohid Khan (i21 - 1424)
Ali Haider (i21 - 1402)
Salman Butt (i21 - 1406)
Haadi Ahmad (i21 -
2613)
FAST NUCES ISLAMABAD CAMPUS
Data-Driven Approach to Visual Search: Enhancing Customer
Ex erience in A
arel Industr Throu h Machine Learnin .
With the rise of online shopping, the number of e-stores has grown significantly, making it essential for brands
to enhance customer experience. This research explores how artificial intelligence (AI) and machine learning
(ML) can improve user engagement in the apparel industry by implementing a visual search system. By
analyzing consumer preferences and shopping behaviors, the study aims to understand how brands can
optimize visual search for a more intuitive and efficient browsing experience. A key challenge in the industry
is the lack of a seamless visual search experience, which often leads to frustration as customers struggle to
find desired products. While visual search technology is gaining traction globally, it has yet to be widely
investigates the gap resulting from limited AI integration and the absence of localized datasets. By leveraging
AI-driven image recognition and feature extraction, we aim to develop an efficient product discovery system
that enables users to upload an image and instantly find similar items, eliminating the need for manual
industry to enhance user experience and seamless online shopping.
Key Words: Visual Search, Apparel Industry, Customer Experience, Feature Extraction, Machine Learning,
Ima e Reco ition.
Supervisor: Ms. Sidro Abbas
Co-Supervisor: Dr. Noumon Noor J Mr. Omar Noeem
Group Members: Naimol Khan Bangash, 211-26181 Zoyo Ali, 211-14151
Usoma Younus, 211- 1419
Data- Driven Approach to Visual Search: Enhancing
Customer Experience in Apparel Industry Through
Machine Learning.
Objective
An Al-driven visual search algorithm t hat can categorize apparel based on
visual similarities to enhance customer shopping experience.
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Area of Stud :
Marketing Analytics
Su ervisor Name:
Ms. Sidra Abbas
Co-Su ervisor Name:
Dr. N ouman Noor
Mr. Omar N aeem
Grou Members:
Naimal Khan Bangash (21i - 2618)
Zoya Ali (21i - 1415)
Usama Younus (21i - 1419)
FAST NUCES ISLAMABAD CAMPUS
Predictive Analytics-Powered Consultancy Model for TenX Private
Limited: Driving Strategic International Expansion and Targeted Client
Acquisition
In this era of constant change, a company's ability to succeed in the global marketplace is
dependent on making data informed decisions. This study seeks to develop a predictive analytics
powered consultancy model that is able to help TenX Private Limited pinpoint specific clients and
regions with the highest potential for growth. Considering the firm's challenges in attaining high-
value consultancy, this examination utilizes machine learning, geospatial analysis, and strategic
methodologies such as SWOT and PESTEL to deliver insights that are easy to implement. With the
intention of providing TenX with a flexible and low-cost global expansion strategy, the study seeks
to combine real-time data tracking with feedback from TenX's stakeholders, thereby resolving the
divide between data science and strategic business expansion.
Predictive Analytics-Powered Consultancy
Model for TenX Private Limited:
DRIVING STRATEGIC GLOBAL EXPANSIO N AND
TARGETED CLIENT ACQUISITION.
Objective 01
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Area of Study:
Management
Supervisor Name:
Dr. Muhammad Abbas
Group Members:
Maha Gardezi (i21-1404)
Muhammad Hanan (i21-1439)
Minahil Nadeem (i21 -2614)
Rimsha Ahmed (i21-2616)
FAST NUCES ISLAMABAD CAMPUS
Forecasting and Strategic Planning for Sustainable Tourism
tourism sector has not reached its full potential due to various challenges, including infrastructure
limitations, policy gaps, and underutilization of data-driven decision-making. This research aims to
develop a comprehensive forecasting model and strategic framework to enhance sustainable
learning techniques, this study will identify trends, predict future tourism growth, and suggest
policy interventions. The study is motivated by the need to bridge the gap between traditional
tourism development strategies and modern data-driven planning. It will assess key factors such as
regional tourism potential, and economic impacts to create actionable insights for policymakers,
businesses, and local communities. The ultimate goal is to provide a strategic roadmap that aligns
Key Words: Tourism, Economy, Competitor Analysis, Sentiment Analysis, Dashboard
SUPERVISOR
MR. HAMMAD MAJEED
FORECASTING AND STRATEGIC PLA NNING
FOR SUSTAINABLE TOURISM DEVELOPMENT
IN PAKISTAN: A DATA DRIVEN APPROACH
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MACROECONOMIC AND COMPETITVE INSIGHTS
SUSTAINABLE TOURISM AND DAT A DRIVEN INSIGHTS
Plan Smarter
Trips
OVERVIEW
BetterTravel
Deals And 8udgeting
Area of Study:
Business Analytics
Supervisor Name:
Mr. Hammad Majeed
Group Members:
Neha Chohan (i21 - 1420)
Laiba lrfan (i21- 1405)
Zaha Asim (121 - 5930)
Zara Ahmad Khan (i21 - 1413)
FAST NUCES ISLAMABAD CAMPUS
Al-Driven Portfolio Optimization:
A Comparative Analysis using Artificial Intelligence and Traditional
Models
This research investigates the optimization of stock investment portfolios using both Al-driven and
traditional methods. The study compares Al techniques such as Genetic Algorithms and
Reinforcement Learning with traditional Modern Portfolio Theory to determine which approach
yields superior risk-adjusted returns. Using historical data from the top 100 stocks in China and
research explores how these approaches can contribute to achieving the United Nations
Sustainable Development Goals (SDGs), particularly in the areas of economic growth, responsible
consumption, and financial innovation. The findings will offer valuable insights into enhancing
portfolio optimization and investment strategies, helping investors and financial analysts make
more informed decisions.
Key Words: Portfolio Optimization, Artificial Intelligence, Genetic Algorithms, Reinforcement Learning,
Modern Portfolio Theory
Al-DRIVEN PORTFOLIO
OPTIMIZATION
A Comparative Analysis using
Artificial Intelligence and Traditional Models
OBJECTIVE
Evaluate the effectiveness of Al techniques,
specifically Genetic Algorithms and
Reinforcement Learning,
alongside traditional Modern Portfolio Theory
to determine which approach yields superior
risk-adjusted returns.
Area of Study:
Finance
Supervisor Name:
Dr. Muhammad Yasir
Group Members:
Areeba Tariq (i21 - 1401)
Zoya Akbar (i21- 1403)
Ayesha Siddiqa (i21 - 1422)
Syeda Farheen Akhtar (i21 - 2602)
FAST NUCES ISLAMABAD CAMPUS
Enhancing Organizational Branding Through People Analytics: Predicting
Employee Turnover Using Machine Learning
This model will bridge the gaps by creating a predictive model with machine learning; it combines
people analytics with the economic and industry external data. Some of the objectives of the model
are:
•Improve the accuracy of the internal/external data combined in the making of turnover forecasts.
• Actionable recommendations that would make the possibility of HR decision-making possible,
meaning the organization would take retention strategies that are custom-tailored according to
specific risk factors related to turnover.
• Turnover prediction goes hand in hand with organizational branding where an organization is
depicted to be an attractive employee-attracting organization for employers: an organizational
leadership in turning over.
In summary, this research fills the theoretical and practical gaps that exist in turnover prediction
with a more holistic, data-driven approach to address both internal and external factors that
influence employee turnover. It therefore not only enhances predictive accuracy but enables
organizations to strengthen their brand as employers in contributing to a sustainable and engaged
workforce.
SUPERVISOR
MR.HAMMAD MAJEED
CO SUPERVISOR
MR.OMARNAEEM
Marketing Meets People Analytics:
Predicting Employee Turnover, Retent ion
Strategies & Enhancing Employer Branding
for Talent Acquisit ion
OBJECTIVES
Analyze Key Drivers of Employee Turnover
Develop Pred1ct1ve Models for Employee Retention
Enhance Employer Branding for Talent Acqu1s1t1on
Develop a Web-Based Real-Time Analytics Interactive
Dashboard
METHODOLOGY
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TOOLS USED
Group Members
Ahmed Nawaz Khanzada 21i-1437
Taha lmtiaz 21i·1433
Azfar Waqas 21i-1412
Farhan Khalid Ahmad 2li-2611
Area of Study:
HR ANALYTICS
Supervisor Name:
Mr. HAMMAD MAJEED
Group Members:
Ahmed Nawaz Khanzada(21i-1437)
Taha lmtiaz(21i-1433)
Azfar Waqas(21i-1412)
Farhan Khalid Ahmad(21i-2611)
FAST NUCES ISLAMABAD CAMPUS
Business plan for a platform to optimize storage space utilization
The project involved creating a business plan for a platform designed to optimize storage space utilization
by connecting individuals and businesses with available storage spaces, akin to an "Airbnb for storage." The
platform aims to revolutionize the storage and warehousing industry by providing a user-friendly solution
for finding and booking storage spaces, enabling those with excess space to rent it out for additional income
while offering renters cost-effective and convenient storage options near their areas of interest. The study
technical, economic, and legal aspects. From a technical perspective, we determined the appropriate
technology stack and assessed the availability of skilled developers and necessary infrastructure to ensure
the application could handle increasing user demand and data volumes. Economically, we explored potential
revenue streams, such as commission fees and data monetization, while estimating development costs,
operational expenses, and marketing budgets to project profitability. We developed robust operating
procedures and user policies, which will clearly define obligations and liabilities for all parties, ensuring
secure and hassle-free operations. The platform not only provides a solution for underutilized storage spaces
but also generates valuable data to understand storage needs across various locations, enabling future
expansion and improved service offerings. This comprehensive study confirmed the viability of the project,
paving the way for its successful implementation in the market.
Key Words: 4th party logistics, warehousing, storage solutions, storage o timization
Area of Study:
Cross Cluster
Supervisor Name:
Mr. Bilal Saeed
Group Members:
Balaj Nadeem Kiani (i21 - 0063)
Khurram Khan (i21 - 0052)
M. Asad Anwar (i21- 0079)
Nisa Zahra (i21 - 0115)
FAST NUCES ISLAMABAD CAMPUS

AI enrichment