Real-or-Not-NLP-with-Disaster-Tweets

Predict which Tweets are about real disasters and which ones are not?

Dataset

-The datasets contains a set of tweets which have been divided into a training and a test set. The training set contains a target column identifying whether the tweet pertains to a real disasteror not.
-Our job is to create a ML model to predict whether the test set tweets belong to a disaster or not, in the form of 1 or 0. This is a classic case of a Binary Classification problem.

Understanding the Evaluation Metric

for this particluar problem, our submissions will be evaluated using F1 between the predicted and expected answers.
-The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.
-The F score is defined as the weighted harmonic mean of the test’s precision and recall.
-Precision, also called the positive predictive value, is the proportion of positive results that truly are positive.
-Recall, also called sensitivity, is the ability of a test to correctly identify positive results to get the true positive rate.
-The F score reaches the best value, meaning perfect precision and recall, at a value of 1. The worst F score, which means lowest precision and lowest recall, would be a value of 0.

Table of Contents of My Code

1. Importing the necessary libraries
2. Reading the datasets
3. Basic EDA
4. Text data processing
5. Transforming tokens to vector
6. Buiding a Text Classification model.