Problem Statement
Develop a classification model that can distinguish which of two subreddits, (r/menstruation or r/BabyBumps), a particular post belongs to.
Executive Summary
We cleaned and analyzed 2 features (post text and post title) which we engineered by way of Count Vectorizing. We removed English stop words, stripped ASCII accents for both features. This resulted in a 393 features which we fed into 2 variations of Logistic Regression, Decision Tree, Random Forest and Multinomial NB models each.
My second Multinomial Naive Bayes model performed the best. With the best parameters being?-?alpha: 0 and fit_prior: False. The accuracy score on the training data was 92.4% and on unseen data it came out to be 92.2%. This means our model is slightly and probably inconsequentially overfit. This also means that 92.2% of our posts will be accurately classified by our model.
Recommendations
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Go with Multinomial Naive Bayes Model
- Version 2 handled bias/variance the best
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Spend more time with current features
- e.g. Engineer a character or word length feature
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Explore new features
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