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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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sentiment-analysis-of-amazon-reviews's Introduction

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!

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

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