Your sentiment analysis repo is the AI resume that gets read

Recruiters click the GitHub link before they finish reading your bullet points, so for an e-commerce sentiment analysis final year project the repo has to explain itself without you standing next to it. The pipeline this kind of project covers — web scraping for live product data, NLP over customer reviews, a neural network for sentiment classification, and a dashboard showing price trends and sentiment scores — is a lot of surface area for one project. That's exactly why the presentation decides whether it reads as hands-on work or as a class assignment nobody can verify.

Make the README the front door

The top-level README.md is read first, so put the project overview, the technologies (Python, TensorFlow or PyTorch, plus whichever NLP libraries you actually used), and the setup and usage instructions there. Usage examples matter more than theory: someone should be able to run your demo straight from the README. If they have to open five files to find the entry point, they usually close the tab instead.

Name the folders after your pipeline stages

Split the code the way the data flows: scraping/ for the scrapers, model/ for the network architectures and training scripts, dashboard/ for the visualization code. That naming tells an interviewer what to expect before they open anything, and it maps cleanly onto how they'll ask questions later.
This is where the project usually falls apart. If model/ ships a saved weights file but no training script, nobody can reproduce your result, and the natural conclusion is that the report did the modeling, not you. Scrapers are just as fragile — e-commerce sites change markup or block bots, so if your scraper no longer runs, don't leave it silently broken. Say so in the README and keep a small saved sample so the model and dashboard still execute end to end. A workflow that runs on frozen data beats one that dies on cell one.

Keep the data small and honest

Sample datasets belong in the repo, not the full scrape. Full review and price dumps hit privacy and repo-size limits, and scraping terms for commercial sites are not something to wave around in an interview. A trimmed example of review and price data is enough to prove the format and let a reviewer run the code.

Let notebooks and screenshots do the demo

Jupyter notebooks are the cheapest way to show exploratory data analysis and model results in order, with outputs already rendered. Pair them with screenshots of the dashboard or the output graphs, because someone skimming on a phone wont run your notebook but will look at a picture of the sentiment breakdown and price comparison. The project report PDF and the PPT also belong in the repo — they're the artifacts a panel may ask you to walk through.

Mirror the repo wording on your resume

On the resume side, keep the language consistent with the README so a cross-checker doesn't find two different stories. An ATS scans for concrete tools and methods — Python, NLP, neural networks, web scraping, dashboards — so name those instead of "machine learning project." One line on what the project does and one line on what you built is enough; the repo carries the depth.

Be ready for the viva, not just the click

The interview almost always goes deeper than the repo. Expect to defend why you chose neural sentiment classification over a simpler lexicon approach, why price comparison sits next to sentiment, and what your dashboard actually shows. If a number in your slides doesn't match the notebook output, that's the moment trust drops.
A repo that runs, a README that spells out the steps, and a resume that names the exact tools will carry the project further than any surface polish.