Need help with attached file Part l: Disaster Tweets classification

For this task, your goal is to build a classifier to classify whether a tweet is about a real disaster $(1)$ or not $(0).$ Your classification task is binary. You can only use the text as feature to classify the tweets $($similar to the sentiment analysis example we used in our class$).$

You will need to process the data, in order to extract relevant features for building a classification model. You need train the model with the given training dataset and evaluat your model with test dataset. You need to get the accuracy, precision, and recall. You may use everything we have covered in class. You donot need to worry if you get a low accuracy.

Writeup. Together with your code, you should submit a short write$-$up$,$ which explains: How did you pre$-$process the data $($if at all$)?$ Which features did you use and why?

Your final submission should include the following:

$-1.$ Python code in jupyter notebook to generate the classification results. $**$ a$.$ Read the training data train.csv $**$ b$.$ Plot histogram for the label column to show the distributions of disaster $(1)$ tweets and no$-$disaster $(0)$ tweets. $**$ c$.$ Process the training data so that it can be used for your machine learning model $**$ d$.$ Specify and train your model $\backslash (\{\}^\{**\}\backslash ).$ Read the test data test.csv$,$ process it$,$ classify the test data, and evaluate your model.

$-2.$ Writeup $($you can also put them in the jupyter notebook$).$

NLP Libraries

There are many open source Natural Language Processing $($NLP$)$ libraries and these are some of them:

$-$ Natural language toolkit $($NLTK$)($Python$)$

We will use Python NLTK library.