MUSTAFA FAISAL KHAN
OTHER-UNCATEGORIZED
· 2026
Email
m.khan.26176@khi.iba.edu.pk
Phone
318/234-0407 / +92 3182340407
GitHub
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Academic
Program
BS in Economics
CGPA
3.54
Year
2026
Education
Institute of Business Administration
Address
Karachi
DOB
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Career
Current role
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Target role
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Skills
finance, policy analysis, data-driven decision-making, analytical skills, research, communication, Content Writing, Python, R, Machine Learning, Data Cleaning, Exploratory Data Analysis, Decision Tree Regression, Linear Regression, Lasso, Ridge, Statistical Analysis, Normality Testing, Residual Diagnostics
Interests / quote
I have a strong interest in finance, policy analysis, and data-driven decision-making which is why I look forward to opportunities to apply analytical, research, and communication skills in a dynamic professional environment while learning from industry experts and creating impact.
Verbatim text
The exact text the LLM saw on the page (or the booklet text from the old import).
This is what powers semantic search.
MUSTAFA FAISAL KHAN Email: m.khan.26176@khi.iba.edu.pk | Phone: 318/234-0407 / +92 3182340407 LinkedIn: https://www.linkedin.com/in/mustafa-faisal-khan/ CAREER OBJECTIVE I have a strong interest in finance, policy analysis, and data-driven decision-making which is why I look forward to opportunities to apply analytical, research, and communication skills in a dynamic professional environment while learning from industry experts and creating impact. EDUCATION BS in Economics – 2026 Institute of Business Administration, Karachi, CGPA: 3.54 A-Level – 2022 Private Candidate, Karachi Grade/Percentage: 78% O-Level – 2020 Private Candidate, Karachi Grade/Percentage: 84% MAJORS Economics INTERNSHIP/WORK EXPERIENCE Content Writer and CYD associate Bryt | Apr 2021 - Aug 2022 Prepared content (50+ resources) and supported live queries by responding in under 30-minutes TERM PROJECT(S)/RESEARCH WORK(S) • UK-EU trade through Gravity Model: This project explores UK-EU trade relations through the lens of the Gravity Model of Trade, analyzing how factors like economic size, distance, and Brexit affected bilateral trade flows. The study incorporates historical trade trends and recent shifts in trade dynamics post-Brexit to evaluate the changing structure of UK-EU economic ties. • Machine Learning for Smartphone Price Prediction in Python: Analysis of smartphone price determinants using a dataset of 1,200+ devices. After cleaning and transforming data (handling missing values, log-transforming prices, and encoding brands), exploratory analysis revealed correlations between price, RAM, storage, and display size. A decision tree regression model outperformed linear, lasso, and ridge models, achieving the highest accuracy (R²=0.77) in predicting prices. • Machine Learning for Camera Prices in R: These files present a statistical analysis of camera data, focusing on variables such as price, megapixels, weight, and brand (Canon vs. Nikon). The analysis includes normality testing (rejecting normality for most variables), linear regression modeling to predict price, and model comparisons using adjusted R-squared and residual diagnostics. The final model (m3) incorporating megapixels, weight, and score showed improved explanatory power, though residual plots indicated potential non-linearities, suggesting limitations in linear assumptions for prediction accuracy. • Security Services Market Structure in Pakistan: This report analyzes Pakistan's evolving security landscape, examining the interplay between public policing challenges and the expanding private security sector. It highlights rising crime rates, economic pressures, and the critical role of public-private partnerships in addressing security gaps through modernization, regulatory reforms, and tailored services. The study underscores the need for strategic investments and collaboration to enhance national safety and economic confidence.