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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.

Home Page: https://movie-recommender-system-by-neha-sharma.streamlit.app/

Jupyter Notebook 98.16% Python 1.84%
bag-of-words content-based-recommendation cosine-similarity data-science machine-learning porter-stemmer similarity-matrix stop-word-removal streamlit-application text-vectorization

movie-recommender-system's Introduction




College Student | Aspiring Data Analyst | Art Appreciator


About Me⭐

🎓Presently a data science student at Noida Institute of Engineering and Technology.

🏡A resident of Delhi and a true Delhite at heart.

☕Like 80% of the student population, I'm caffeine addicted and sleep deprived.

♾Balancing the art of life and learning, one juggle at a time.

⚡BTW, Did you know? Pigs can't look up at Sky (><)


My Tech Stack💻

Programming Languages

CSS3 HTML5 Java JavaScript PHP Python R

Frameworks, Platforms and Libraries

Bootstrap Django Flask OpenCV WordPress

Databases

ApacheCassandra MicrosoftSQLServer MongoDB MySQL SQLite

Design

Adobe Adobe Acrobat Reader Adobe Creative Cloud Adobe Illustrator Adobe Photoshop Canva Figma

Machine Learning / Deep Learning

Keras Matplotlib NumPy Pandas Plotly scikit-learn Scipy TensorFlow

Others

Power Bi Tableau

Reach Me At📫


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