CodeClauseInternship_Movie-Genre-Prediction

Movie Genre Prediction

This repository contains a machine learning project that predicts movie genres based on plot descriptions.

Overview

The project utilizes natural language processing (NLP) techniques to preprocess and analyze movie plot descriptions. It then builds a machine learning model using the Multinomial Naive Bayes algorithm to predict the genre(s) of movies based on their plots.

Dataset

The dataset (movies.csv) used in this project contains movie details including plot descriptions and genres. It was preprocessed to clean and tokenize the plot descriptions before training the model. Data was sourced from Wikipedia Movie Plots Dataset on Kaggle

Dependencies

• Python 3.x
• Libraries:
  • pandas
  • numpy
  • scikit-learn
  • spacy
  • matplotlib
  • seaborn
  • joblib
  • wordcloud

Project Structure

• data/: Contains the dataset (movies.csv) used in the project.
• models/: Stores the trained machine learning model (movie_genre_classifier.pkl).
• notebooks/: Jupyter notebooks used for data exploration, preprocessing, model training, and evaluation.
• README.md: This file, providing an overview of the project, instructions, and dependencies.

Usage

1. Clone the repository:

   git clone https://github.com/vedant713/CodeClauseInternship_Movie-Genre-Prediction.git
   
   

2.Install dependencies:

      pip install -r requirements.txt

3.Navigate to notebooks/ directory:

Explore the Jupyter notebooks to understand the data preprocessing, model training, and evaluation steps. Run the notebooks: Execute the notebooks sequentially to reproduce the steps and results of the movie genre prediction project. Train and save the model: Once satisfied with the model performance, train the final model using the provided notebooks and save it using joblib. Predict movie genres: Use the trained model '(movie_genre_classifier.pkl)' to predict genres for new plot descriptions.