aishwarya-ml-package

Create a personalized movie recommendation system using the 'aishwarya-ml-package' library. This system will take user input such as movie genres they enjoy and the ratings of movies they have seen, then recommend new movies based on their preferences. Here's a step-by-step guide on how to build it:

1. **Data Collection**: Start by gathering a dataset of movies and their associated genres, directors, actors, and user ratings. You can use a public dataset like the IMDb dataset available on Kaggle.
2. **Data Preprocessing**: Use the preprocessing module from 'aishwarya-ml-package' to clean the data. Handle missing values, encode categorical variables, and normalize numerical data if necessary.
3. **Feature Extraction**: Extract relevant features such as genre, director, actor, and rating. Consider using PCA from 'aishwarya-ml-package' to reduce dimensionality if the feature space is too large.
4. **Model Training**: Implement a recommendation model using either regression or KNN algorithms provided by 'aishwarya-ml-package'. Train the model on the preprocessed and feature-extracted data.
5. **Pipeline Creation**: Utilize the pipeline module from 'aishwarya-ml-package' to streamline the preprocessing and model training steps into one process, ensuring reproducibility and efficiency.
6. **User Interface**: Develop a simple command-line interface where users can input their favorite movie genres and receive recommendations based on the trained model.
7. **Evaluation**: Test the recommendation system with various inputs and evaluate its performance using metrics like accuracy and user satisfaction.
8. **Deployment**: Once satisfied with the performance, consider deploying the application as a web service or a mobile app for broader accessibility.

The goal is to create an engaging and accurate recommendation system that enhances the movie-watching experience for users.