---
language:
- en
metrics:
- accuracy
tags:
- sklearn
- machine learning
- movie-genre-prediction
- multi-class classification
---

## Model Details

### Model Description

The goal of the competition is to design a predictive model that accurately classifies movies into their respective genres based on their titles and synopses.

The model takes in inputs such as movie_name and synopsis as a whole string and outputs the predicted genre of the movie.



- **Developed by:** [Shalaka Thorat]
- **Shared by:** [Data Driven Science- Movie Genre Prediction Contest: competitions/movie-genre-prediction]
- **Language:** [Python]
- **Tags:** [Python, NLP, Sklearn, NLTK, Machine Learning, Multi-class Classification, Supervised Learning]

### Model Sources

- **Repository:** [competitions/movie-genre-prediction]

## Training Details

We have used Multinomial Naive Bayes Algorithm to work well with Sparse Vectorized data, which consists of movie_name and synopsis.
The output of the model is a class (out of 10 classes) of the genre.

### Training Data

All the Training and Test Data can be found here:

[competitions/movie-genre-prediction]

#### Preprocessing

1) Label Encoding
2) Tokenization
3) TF-IDF Vectorization
4) Preprocessing of digits, special characters, symbols, extra spaces and stop words from textual data

## Evaluation

The evaluation metric used is [Accuracy] as specified in the competition.