This project aims to recommend movies based on the idea that users will like items with similar features to those they have liked before. We used cosine similarity to calculate the similarity between the movies. We utilized the Streamlit library in Python to implement the web application..
- Data: Contains the dataset used for training and testing the classification model.
- Notebooks: Jupyter notebooks for data exploration, feature engineering, model training, and evaluation.
- Scripts: Python scripts designed for preprocessing data and making predictions within the WebApp for handling new data.
- Models: Saved trained models for future use or deployment.
video1.mp4
👋 Hi there! I'm Sinchana Chatterjee, an enthusiastic and determined B.Tech student with a fervent aspiration to excel as a Data Scientist and Data Analyst.
- pandas
- numpy
- sklearn
- matplotlib
- seaborn
- Thanks to kaggel for providing the dataset used in this project.
- Special thanks to the open-source community for their invaluable contributions to the tools and libraries used in this project.
If you have any feedback, please reach out to me at csinchana19@gmail.com