i must discover the many predictive key words and/or phrases to accurately classify the the dating advice and relationship advice subreddit pages so we may use them to ascertain which adverts should populate for each page. Because this is a category issue, I’ll utilize Logistic Regression & Bayes models. Misclassifications in this full instance could be fairly safe thus I will utilize the precision rating and set up a baseline of 63.3per cent to price success. Making use of TFiDfVectorization, I’ll get the function value to find out which terms have actually the prediction power that is highest for the mark factors. If successful, this model is also utilized to focus on other pages which have comparable regularity associated with the words that are same expressions.
After switching most of the scrapes into DataFrames, they were saved by me as csvs that you can get when you look at the dataset folder with this repo.
Information Cleaning and EDA
Preprocessing and Modeling
First effort: logistic regression model with default CountVectorizer paramaters. train rating: 99 | test 75 | cross val 74 Second attempt: tried CountVectorizer with Stemmatizer preprocessing on first group of scraping, pretty bad rating with a high variance. Train 99%, test 72%
Simply increasing the information and y that is stratifying my test/train/split increased my cvec test score to 81 and cross val to 80. Incorporating 2 paramaters to my CountVectorizers helped a great deal. A min_df of 3 and ngram_range of (1,2) increased my test score to 83.2 and get a cross val to 82.3 nonetheless, these rating disappeared.
we customized the end terms to just just take the ones away that have been actually too regular to be predictive. It was a success, nonetheless, with increased time we most likely could’ve tweaked them a little more to improve all ratings. Taking a look at both the solitary terms and terms in sets of two (bigrams) had been the best param that gridsearch proposed, nevertheless, most of my top many predictive terms finished up being uni-grams. My list that is original of had a good amount of jibberish terms and typos. Minimizing the # of that time period term ended up being needed to show as much as 2, helped be rid of the. Gridsearch additionally recommended 90% max df rate which Michigan payday loans assisted to get rid of oversaturated terms also. Lastly, establishing max features to 5000 reduced cut down my columns to about one fourth of whatever they had been to simply concentrate the essential frequently employed terms of the thing that was kept.
Summary and Recommendations
therefore I think the model is prepared to introduce a test. The same key words could be used to find other potentially lucrative pages if advertising engagement increases. I came across it interesting that taking out fully the overly used terms assisted with overfitting, but brought the precision rating down. I do believe there clearly was probably nevertheless space to relax and play around with the paramaters regarding the Tfidf Vectorizer to see if various end words make a different or
Used Reddit’s API, demands collection, and BeautifulSoup to scrape articles from two subreddits: Dating guidance & union information, and trained a binary category model to anticipate which subreddit confirmed post originated in