Skip to content

Latest commit

 

History

History
44 lines (30 loc) · 1.48 KB

README.md

File metadata and controls

44 lines (30 loc) · 1.48 KB

Multiclass Text Classification with Machine Learning and Deep Learning

This repository contains code for implementing various machine learning and deep learning models for multiclass text classification. The models implemented in this repository include support vector machines(SVM), Multinominal naive Bayes, logistic regression, random forests, ensembled learning, adaboost, gradientboosting, convolutional neural networks(CNN), and recurrent neural networks(RNN) an gted recurrent unit(GRU).

Requirements:

  • Python

  • Scikit-learn

  • TensorFlow

  • Keras

Dataset:

The dataset used in this project is the bbc-tex dataset, which consists of approximately 2225 text.

Results:

The results of each model on the bbc-text dataset are as follows:

Model Accuracy
Logistic Regression 96.58%
Support Vector Machine 96.94%
Multinomial Naive Bayes 94.97%
Randomforest 95.15%
GradientBoostingClassifier 94.25%
Ensemble Classifier 97.12%
AdaBoost 94.43%
LSTM 1-Layer 99.22%
LSTM 2-Layers 97.78%
GRU 91.74%
CNN+LSTM 98.73%
BERT 99.60%
XLNet 99.46%

Webapplication:

This web application for multiclass text classification using machine learning and deep learning would allow users to input text data and receive a prediction of the most likely category or label for that text.

Webapplication Interface:

interface