Task: Disaster-Tweets-Classifications

Natural Language Processing with Disaster Tweets

Highlights:

• This is a Binary-class text classification (sentence classification) problem.
• The goal of this project is to **Predict which Tweets are about real disasters and which ones are not **.
• This model was built with ML CLassifierRandom Forest s on SKlearn.

Data: Kaggle Dataset for Diaster Tweets

• Input: Train.csv
• Output: submision.csv
• Examples:

id keyword location text target 0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... 1 1 4 NaN NaN Forest fire near La Ronge Sask. Canada 1 2 5 NaN NaN All residents asked to 'shelter in place' are ... 1 3 6 NaN NaN 13,000 people receive #wildfires evacuation or... 1 4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... 1

Python Libraries Used:

• Pandas
• Numpy
• Seaborn
• Matplotlib
• NLTK
• SKlearn

Accuracy:

• ACCURACY : 0.8010505581089954

CONFUSION MATRIX:

CLASSIFICATION REPORT::

           precision    recall  f1-score   support

       0       0.77      0.93      0.85       886
       1       0.87      0.62      0.72       637

accuracy                           0.80      1523

macro avg 0.82 0.78 0.78 1523 weighted avg 0.81 0.80 0.79 1523