This is a Movie Recommender System built using Streamlit and Python. It recommends similar movies based on user input and displays movie details.
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Updated
Sep 11, 2023 - Jupyter Notebook
This is a Movie Recommender System built using Streamlit and Python. It recommends similar movies based on user input and displays movie details.
Extracted users' reviews from Amazon.com and performed sentiment analysis to determine which console to purchase
I used CountVectorizer to convert movie data into vectors, Cosine Similarity to find similar movies, and PorterStemmer to clean the text data for better accuracy in recommendations.
Restrictions on sharing as advised by DataCamp
natural language processing techniques applied on hotel review dataset
Implementation of the PorterStemmer algorithm uploaded to Maven
Web scraping involves extracting data from websites. Text processing techniques like tokenization, stemming, lemmatization, and removing stopwords refine raw text for analysis.
Revolutionize your movie choices with our intelligent Movie Recommendation System, providing personalized suggestions for an unparalleled cinematic experience.
Developed a text classification model to classify SMS as spam or nonspam using Python.
A native Go implementation of the Porter Stemmer Algorithm for the italian language.
Amazon reviews Sentiment Analysis
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