Detect fake news headlines with machine learning

Research question

You write thisOne question you can answer with data, narrow enough to finish in the time you have.

Hypothesis

You write thisWhat you expect to find, and why.

Variables

You write thisWhat you change or compare, what you measure, and what you hold constant.

Methodology

You write thisDescribe the method in enough detail that someone else could repeat it: sample, tools, procedure.

Data collection

You write thisSay what you will record, how many times, and how you will keep the records safe.

Analysis

You write thisChoose the table, chart or test that answers the question; report averages with their spread.

Limitations

You write thisList what could have affected the result: small sample, instrument limits, things you could not control.

Literature review

Find five to eight sources (your textbook, review articles, reports) and note what each says about your question. Group them by idea, not one after another, and end with what is still not known.

References

List every source you used in one style throughout (author, year, title, where it was published, link and the date you opened it).

Use this as the outline of your README or case study.

Problem

You write thisThe problem in one or two sentences.

Why it matters

You write thisWho has this problem and what it costs them.

What was built

A model, its accuracy in and out of distribution, and a critical discussion of what "detecting fake news" can mean.

Technology

Python, scikit-learn

Architecture

You write thisOne diagram: the parts and how data moves between them.

Implementation

You write thisThe 5 build steps in your own words, with one code or design decision you are proud of.

Challenges

You write thisThe hardest bug or decision, and how you got past it.

Results

You write thisNumbers: accuracy, speed, users, survey answers.

Demo and code

You write thisA link to a live demo or a short video, and to the GitHub repository.

Lessons learned

You write thisWhat you would do differently next time.

Future improvements

Remove source-specific words and retrain; report what changes.

Problem

Train a classifier on a public dataset and show why it learns the source's style, not the truth.

Target user

You write thisOne specific person: who they are and when they would use this.

Solution

Train a classifier on a public dataset and show why it learns the source's style, not the truth.

First version (MVP)

You write thisThe smallest version that shows the idea working once, end to end.

Tech stack

Python, scikit-learn

Architecture

You write thisA quick sketch of the parts and how they connect.

Demo

You write thisThe one scenario you will show live, start to finish, in under two minutes.

Stretch goals

Remove source-specific words and retrain; report what changes.

Team roles

FrontendBackendData or modelDesignTestingDocumentationPresentation

Plan by length