Classification systems

Datathon – HackNews – Solution – Insight Hunters

In order to do the following we have to undergo the process of text cleaning, understanding the text. We had to find a way in order to split the data and form a data frame which consists of the following columns.News_TextNews_NumberNews_TypeThe data has lots of fillers which had to be removed and some rows where news_numbers and type were missing. In order to clean the data we had to remove the fillers using the NLTK stop words filtration. Later on we tokenized the data using the word_tokenizer from the nltk package.The next important step was to lemmatize/stem the data to remove the tense from the words and normalize the words. Even though it was a time consumption process the results were promising.XGBoost has capability to handle the imbalanced dataset by using the parameter Scale_Pos_weight. we can do threshold probability throttling to increase the sensitivity by sacrificing the reasonable specificity.Evaluation:- This process is kind of tricky for the train data set provided, as the data was highly imbalanced, the dependent feature/variable had imbalanced classes


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