Assessfy Foundation Projects Lab Beginner 5 milestones 100 marks

Analyzing Product Reviews: Sentiment Detection Made Simple

Discipline: AI & Machine Learning Level: Foundation Industry: AI & Data Function: Engineering Team: up to 3 Assessment: 5 milestones (100 marks)

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Core skills

About this project

What you'll build: In this project, you'll create a basic Python program that reads product reviews and automatically tells you if each review is positive or negative. You'll use a beginner-friendly natural language processing (NLP) library to make it easy!

Why it's a good starter: This project is perfect for first- or second-year students because it focuses on one practical skill, has clear steps, and uses free tools. You'll get hands-on experience without needing any advanced knowledge.

What you'll learn: You'll pick up the basics of handling text data, applying sentiment analysis, and presenting your results. Along the way, you'll improve your Python skills and learn how to use popular AI libraries.

Milestones

1. Setup & Plan

10 marks 5d

Submit a short plan outlining your approach, and confirm your Python/NLP environment is ready (with TextBlob and Jupyter/Colab working). A screenshot showing successful library installation. (You've chosen sample product reviews and checked you can load them.)

2. Core Build, Part 1: Loading & Analyzing Reviews

30 marks 10d

Submit Python code that loads product reviews from a file or list and uses TextBlob for basic sentiment analysis. Results are shown for each review (positive/negative). A Jupyter notebook with working code and sample results.

3. Core Build, Part 2: Features & Improvements

30 marks 10d

Add simple features: count positives/negatives, show average sentiment, and tidy up code. Submit updated notebook and a sample output screenshot. (You refined your analysis and made results clearer.)

4. Testing & Polish

15 marks 7d

Submit evidence of testing: try your code with different review samples, fix any bugs, and improve readability. Show before/after screenshots and a short list of changes made.

5. Demo & Short Report

15 marks 7d

Submit a brief report (1-2 pages) explaining what you built, what worked well, and what you learned. Include a demo (screenshots or video) showing your final program in action.

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Skills you'll learn

AI & Machine LearningReading and processing text data in PythonUsing a beginner-friendly NLP library (like TextBlob)Performing simple sentiment analysisDisplaying results in a clear formatTesting and refining your code

Tools used

Python 3TextBlob library (free)Jupyter Notebook or Google ColabPandas (for data handling)

Prerequisites

Basic programming in PythonSchool-level maths (for simple calculations)Curiosity to explore AI tools

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Analyzing Product Reviews: Sentiment Detection Made Simple

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