Overview
This project is a Movie Recommendation System built using Machine Learning algorithms.
It suggests movies based on the input movie title provided by the user.
The app integrates a machine learning model trained on movie data, allowing users to discover new movies similar to their preferences.
The project includes two main components:
app.py: A Streamlit application that provides a user-friendly interface for interacting with the recommendation system.
Jupyter Notebook: A notebook containing the detailed implementation of the machine learning model, data preprocessing, and exploratory analysis.
Libraries Used
- Pandas: For data manipulation and preprocessing.
- Numpy: For numerical computations.
- Scikit-learn: For TF-IDF vectorization and similarity calculations.
- Streamlit: For building the web app interface.
- Jupyter: For the development and testing of the recommendation model.