Title: Predicting Disaster Tweets Using Natural Language Processing and Logistic Regression The project assignment again comes from the Kaggle site. This assignment is a logistic regression analysis...

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Title: Predicting Disaster Tweets Using Natural Language Processing and Logistic Regression The project assignment again comes from the Kaggle site.  This assignment is a logistic regression analysis to classify tweets as Natural Disaster tweets.  As with previous assignments, much of the code you will need can be found using URL links below.  Your main responsibility is to provide complete end to end assessment of your model results, explain for the reader what is occurring, how do different models compare in terms of outcome, why are some models potentially better for this application than others. Most importantly, this interpretation must be understandable for someone less familiar with machine learning.  This assignment will introduce some new libraries.  Any library you encounter that you have not previously installed, you will need to install with install.packages().  You will also encounter some Natural Language Processing (NLP) terminology.  Consider using the URL below to assist in gaining some understanding that you can convey for the reader. URL links below provide... · Overview of the original Kaggle competition, https://www.kaggle.com/c/nlp-getting-started/overview/description (Links to an external site.)  https://www.kaggle.com/c/digit-recognizer/overview/description (Links to an external site.) · Overview of the test and train data sets, https://www.kaggle.com/c/nlp-getting-started/data (Links to an external site.) (Links to an external site.) · Tutorials related to the competition, https://www.kaggle.com/wrrosa/nlp-getting-started-tutorial-in-r (Links to an external site.) (Links to an external site.) · Basic understanding of NLP terminology and R, http://rstudio-pubs-static.s3.amazonaws.com/256588_57b585da6c054349825cba46685d8464.html (Links to an external site.) Considerations for this assignment · Classify tweets as Disaster or not using a variety of features · Use ROC curves and confusion matrix results to compare multiple models · Explain your results including critical metrics like accuracy, precision, recall · Compare your test results to training results; does it indicate concerns for overfitting? · You can and likely should consider creating new features from existing features · Explain how and why you derived any new features; what did you see or theorize that led to your decision Expectations · Your work must be reproducible.  This means every step you take along the way from importing data sets, to creating train and test, to creating or modifying features, to generating the models; everything must be in the RMD so that an independent researcher could recreate your work. · Explain data preparation steps taken.  · Your work must be done with R through RMD and knitted to a pdf.  · I would like you to build all models using the caret library and train() function. · Use kfold cross validation through caret with k of your choice; it is fine to rely on k=10; just be consistent.  · Interpret the results.  · It is critical that you provide the client feedback for each model as to how the model will perform out of sample, with your test set.  · The Kaggle site has already divided the data into train and test.  Do not divide them further.  Train the models using train.csv, and use test.csv to predict and evaluate out of sample metrics · You are encouraged but not required to create a Kaggle account and participate in the Getting Started competition for the housing data. 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May 01, 2021
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