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Name: Nabeel Akram

Type: User

Location: London, United Kingdom

Blog: https://www.datascienceportfol.io/nabeelakram

About Nabeel Akram

Welcome to My Profile!

👨‍💻 I'm Nabeel, a dedicated and passionate Data Analyst with a robust educational background and diverse professional experience in the field of data science and analytics. Welcome to my GitHub space where I share my projects, ideas, and the continuous journey of learning and exploring the vast world of data.

Education 🎓

MSc in Data Science and Analytics with Advanced Research
- University of Hertfordshire, Hatfield, UK (Feb 2021 – Feb 2023)
- Specialty areas: Data Mining, Neural Networks, Machine Learning.
BSCS in Computer Science
- University of Sargodha, Lahore, Pakistan (Sep 2015 – Jun 2019)
- Focus: Artificial Intelligence, Database Systems, Statistical Analysis.

Professional Experience 💼

Quantium, London, UK (Data Analytics Intern)
Accenture, London, UK (Data Analytics and Visualization Intern)
KPMG, London, UK (Data Analytics Intern)

In these roles, I have honed my skills in data analysis, visualization, and turning complex datasets into actionable business insights, significantly impacting business strategies and decision-making processes.

Skills and Competencies 🛠️

Data Visualization & Reporting: Proficient in SQL, Python, R, Power BI, and Excel.
Database Management: Experienced in MySQL and Microsoft SQLServer.
Soft Skills: Strong communicator, team player, adaptable.

Projects and Contributions 🌟

My projects range from analyzing London bike sharing data to developing interactive dashboards in Tableau and Excel. These works demonstrate my capability to handle complex data and translate it into meaningful, user-friendly visualizations.

London Bike Sharing Analytics
Interactive Coffee Sales Insights Dashboard
British Airways Review Dashboard

Let's Connect!

I am always open to collaborating on projects or discussing data and analytics. Feel free to reach out!

📧 [email protected]
🔗 Linkedin
🖊️ Medium
🌍 Portfolio
📍 London, United Kingdom

Thank you for visiting my profile, and I look forward to connecting with you!

Nabeel Akram's Projects

fiverr-gig

https://www.fiverr.com/iamnabeelmughal/design-unique-flat-minimalist-logo-design

hello-world

just another repository

iamnabeelmughal

interactive-coffee-sales-insights-dashboard

This repository details the development of an interactive coffee sales dashboard using advanced Excel features. It encompasses the complete journey from data management and analysis to the integration of interactive features.

london-bike-sharing-analytics

This project demonstrates a comprehensive data analysis workflow, leveraging Python's pandas library and Tableau for data manipulation and visualization. The focus is on the London bike sharing dataset, showcasing a dynamic, interactive Tableau dashboard.

nabeelakram

My Personal Portfolio Website

nabeelakram.github.io

My Personal Portfolio Website

nabeelportfolio

ocr-free-transformers

Interpreting invoices is difficult with current costly, inflexible OCR-based VDU methods. We propose an OCR-free VDU model using a Transformer architecture and 'Bart-model' decoder to address these challenges.

portfolio

retail_strategy_and_analytics

Optimizing Chip Sales Through Customer Segment Analysis & Store Layout Trials

sentiment-analysis-of-amazon-reviews

Online reviews play an important role in today’s eCommerce industry. Product comments, ratings, posts, etc. have become crucial for a product’s success. People tend to buy products that have more ratings and favorable comments. However, fake reviews can be used to mislead users. Malicious users can post fallacious ratings and comments to any product which may result in degrading its overall ratings and consequentially damaging the customer’s trust. Thus, detecting & classifying these ratings and comments as real or fake has become mandatory for the effectiveness of business opportunities associated with eCommerce industry. A lot of researchers have published different techniques primarily for the detection of fake reviews. Some suggest the use of linguistics while others suggest the use of behavioral analysis. In this report, we use the optimal method for classifying the human sentiments and later the classified the reviews as real or spam. We applied different machine learning algorithms like Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, and Neural Networks on our dataset. This application gave performance results of each algorithm that were measured on the basis of parameters like precision, recall, and f-measure. Finally, a web prototype was developed to showcase the results.

tableau-dashboard-for-british-airways-reviews

This repository documents the creation of an interactive Tableau dashboard focusing on British Airways reviews. This project showcases my expertise in data visualization, integration, and public sharing of insightful analytics.