Skip to content
SHYAMFRANCISPublic

About

Movie Genre Classification Using Machine Learning A text-based classification system that predicts the genre of a movie based on its plot summary using traditional NLP techniques and machine learning models. Built using Python, Scikit-learn, and NLTK.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Latest commit

 

History

1 Commit

Folders and files

NameName
Last commit message
Last commit date
 
 

Repository files navigation

-CODSOFT

Status: Empty repository — no source files published yet.

This repository (SHYAMFRANCIS/-CODSOFT) currently contains no code. This README is a placeholder grounded only in the repo's GitHub description and the empty file listing — no implementation is documented because none exists yet.

Intended scope (from repo description only)

Per the repository description, the planned direction is a movie-genre classifier from plot summaries using traditional NLP and machine learning (Python, scikit-learn, NLTK). That description is aspirational until code lands; there is no dataset, script, or notebook in the repo to verify it against.

Note: a separate, non-empty repo SHYAMFRANCIS/-CODSOFT- holds three ML practice scripts (movie genre, SMS spam, credit-card fraud). This repo is distinct and empty.

Repository status

  • Visibility: public
  • Default branch: none (this file targets main, created on first push)
  • Contents: none

Getting started (placeholder)

git clone https://github.com/SHYAMFRANCIS/-CODSOFT.git
cd -CODSOFT
# No code to run yet.

The maintainer will document prerequisites, dataset source, installation, and usage here once files are pushed.

Contributing

Issues and pull requests are welcome once source files are published.

License

No LICENSE file present yet. All rights reserved unless a license is added.

About

Movie Genre Classification Using Machine Learning A text-based classification system that predicts the genre of a movie based on its plot summary using traditional NLP techniques and machine learning models. Built using Python, Scikit-learn, and NLTK.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Contributors