Machine Learning model using concepts of NLP to detect troll questions
Description:
As a last-ditch attempt to give Yahoo Answers some legitimacy in the era of Quora and Reddit, Yahoo's CEO Merissa Meyer has tasked you with creating a machine learning model to detect spam and troll questions so that they can be removed. To this end, you are given a hand-labeled dataset consisting of questions with unique IDs and whether they are troll questions or not.
Evaluation:
The evaluation metric for this competition is the F1 score.
The F1 score is the harmonic mean of precision and recall.
The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
Team Name : Kuch_toh_karunga
Team Members :
- Samarth Gattu: https://github.com/sam-25
- Chaithanya Reddy: https://github.com/chaithanya99
Approach : Refer Report and presentation slides.
Kaggle Link : https://www.kaggle.com/competitions/yahoo-troll-question-detection/leaderboard
Kaggle Score :
- Public leaderboard: 0.64902
- Private leaderboard: 0.63762
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