Disaster Tweets Classifications by Machine Learning, which is currently Kaggle Competition.

• train.csv and test.csv files can be found via https://www.kaggle.com/competitions/nlp-getting-started/data.
• Columns in `train.csv' dataset are:
  • id
  • text
  • location
  • keyword
  • target
• You will be predicting if tweet is a real disaster (1) or not (0).
• Machine learning models such as LightGBM, XGBoost, RandomForest, and CatBoost Classifiers have been used to predict the disaster tweets.
• RandomizedSearchCv is used to tune hyperparameters for models.
• There is a commented out code in jupyter notebook in which you can combine other features with tf-idf matrix using hstack just in case of use if wanted.

Models	LGBMClassifier	CatBoostClassifier	XGBClassifier	RandomForestClassifier
Accuracy	0.7634	0.7706	0.7648	0.7873

• RandomForestClassifier has demonstrated higher accuracy than rest of the models. Therefore, Test data is evaluated using RandomForestClassifier.