Multi-label classification of Wikipedia comments. The purpose is to predict the toxicity of these comments among 6 categories :
- toxic
- severe_toxic
- obscene
- threat
- insult
- identity_hate
Dataset available on https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
- toxicCommentClassification.ipynb : Jupyter notebook
- ML_model : folder of Python scripts
- model.py : saves a Linear SVC model trained on the dataset with Pickle (finalized_model.sav)
- f1_score_test.py : loads trained model and evaluates the f1_score on the test dataset
- dev_tests.py : loads trained model and gives predictions of toxicity
- user.py : loads trained model and gives predictions of toxicity for a console entry
- Exploratory data analysis
- Data preprocessing
- Natural Language Processing
- Standard CountVectorizer
- Tfidf Vectorizer
- Multi-label classifier using a MultiOutputClassifier from sklearn.multioutput
- Machine learning algorithms
- Multinomial Naive Bayes Classifier
- Linear Support Vector Machine Classifier
- Logistic Regression
- Evaluation of the f1 score (weighted)
- Parameters tuning with GridSearch CV
- Implement machine learning algorithm using Scikit-learn
- Implement deep learning algorithm using Keras
- Deploy model