https://github.com/bilalhassankhan007/ml-movie-recommendation-system

Movie recommedation system deploy on Heroku
https://github.com/bilalhassankhan007/ml-movie-recommendation-system

cosine-similarity deployment jupyer-notebook kaggle pandas text-mining

Last synced: 22 days ago
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Movie recommedation system deploy on Heroku

• Host: GitHub
• URL: https://github.com/bilalhassankhan007/ml-movie-recommendation-system
• Owner: bilalhassankhan007
• Created: 2024-03-03T10:34:50.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-05-05T15:06:05.000Z (8 months ago)
• Last Synced: 2024-05-05T16:24:01.654Z (8 months ago)
• Topics: cosine-similarity, deployment, jupyer-notebook, kaggle, pandas, text-mining
• Language: Jupyter Notebook
• Homepage:
• Size: 1.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# movie-recommender-system-imdb-dataset

A content based movie recommender system using cosine similarity

• Data Acquisition and Preprocessing: Acquired movie dataset from Kaggle website and Conducted thorough data preprocessing to handle missing values, clean data, and perform necessary transformations for analysis.

• Feature Engineering and Vectorization: Utilized feature engineering techniques to extract relevant features from the dataset and Implemented vectorization methods to convert textual data into numerical format suitable for machine learning algorithms

• Model Building and Evaluation: Employed machine learning algorithms in Jupyter Notebook for model building and Evaluated model performance using appropriate metrics to ensure accuracy and effectiveness.

• Cosine Similarity Implementation: Implemented cosine similarity technique to identify similar movies or texts within the dataset and Leveraged cosine similarity calculations to enhance the accuracy and relevance of movie recommendations for users.

• Website Development: Developed a user-friendly website interface using PyCharm IDE and Implemented frontend components to facilitate user interaction and display recommendations.

• Deployment on Heroku: Deployed the movie recommendation system website on the Heroku platform and Configured deployment settings and resolved dependencies to ensure smooth deployment process.