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porter-stemmer

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Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.

  • Updated Apr 24, 2024
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A Machine Learning Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like MultinomialNB, LogisticRegression, SVC, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, XGBClassifier to compare accuracy an

  • Updated Feb 28, 2024
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A complete research and comparison of text mining concept using various stemming techniques(NLP). With the help of a case study (Twitter Sentiment Analysis) we have tried to analyse the output and taken the results.

  • Updated Jan 9, 2024
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The MOOC Recommender System utilizes NLP techniques for course recommendations in Massive Open Online Courses (MOOCs). It processes raw data, leveraging Tokenization, Porter Stemming, Cosine Similarity, etc., to extract tags from course descriptions, summaries, syllabuses, instructors, and subjects.

  • Updated Aug 19, 2023
  • Jupyter Notebook

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