iamnabeelmughal,Nabeel Akram,github

Name: Nabeel Akram

Type: User

Location: London, United Kingdom

🔍 Data Analyst | Python, SQL & Tableau Expert | MSc Data Science | Driving Insights & Enhancing Decision-Making 📊

As a passionate Data Analyst with hands-on experience at Quantium, Accenture, and KPMG, I specialize in transforming data into actionable insights. My expertise lies in data validation, strategic market analysis, and advanced reporting, proven by significant enhancements in business processes and decision-making. Adept in Python, SQL, R, and data visualization tools like Tableau and Power BI, I excel in extracting and interpreting complex data to inform strategic decisions.

🎓 MSc in Data Science and Analytics | BSCS in Computer Science 📈 Proven track record: Improved data accuracy by 30%, user engagement by 40%, and customer targeting by 35% 🌐 Continuously expanding my skill set in data analysis, machine learning, and database management.

I'm eager to contribute to a team where data-driven solutions are at the forefront of business success.

Nabeel Akram's Projects

fiverr-gig

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

hello-world

just another repository

nabeelakram

My Personal Portfolio Website

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.

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.

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