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https://github.com/virajbhutada/movie-recommendation-system

Embark on a journey through personalized movie insights with "Movie Match" and delve into predictive modeling with "Micro-Numerosity Analysis." Uncover demographic features and reviews, blending sophistication with actionable intelligence in YBI Foundation's Analytics Hub.
https://github.com/virajbhutada/movie-recommendation-system

actionable-insights customer-purchase-predection data-analytics machine-learning movie-recommendation-system

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Embark on a journey through personalized movie insights with "Movie Match" and delve into predictive modeling with "Micro-Numerosity Analysis." Uncover demographic features and reviews, blending sophistication with actionable intelligence in YBI Foundation's Analytics Hub.

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README 

        



#  MOVIE MATCH 🎬



## Project Overview



**Movie Match** is your personalized movie recommendation system designed to help you discover new films that align with your preferences. Leveraging the **Close Match algorithm**, Movie Match delivers highly accurate movie suggestions by analyzing user input and comparing it against a comprehensive movie dataset.



## Dataset



The dataset utilized for this project is sourced from the **YBI Foundation Kaggle repository**. You can access the dataset [here](https://github.com/YBIFoundation/Dataset/raw/main/Customer%20Purchase.csv). This dataset contains detailed information about movies, including:



- **Movie Titles**: The name of the movies.

- **User Ratings**: Ratings given by users, reflecting movie popularity and viewer satisfaction.

- **Genres**: Categories under which the movies fall, helping to refine recommendations.

- **Other Relevant Features**: Additional attributes necessary for constructing a robust recommendation system.



## How It Works



### Close Match Algorithm

- **Approximate String Matching**: Movie Match employs the Close Match algorithm to identify approximate string matches. This feature accommodates minor input variations, such as typos or incomplete titles, ensuring users receive relevant recommendations even with imperfect queries.



### Personalized Recommendations

- **Input Your Favorite Movie**: Users can input their favorite movie title into Movie Match.

- **Curated Recommendations**: Based on the input, the system generates a tailored list of movie recommendations that align with the user’s tastes.



## Usage Instructions



To get your personalized movie recommendations, follow these steps:



1. **Access the Dataset**: Click on the dataset link above to explore the movie database.

2. **Input Your Favorite Movie**: Enter the title of a movie you love into the Movie Match system.

3. **Receive Recommendations**: Enjoy a handpicked selection of movies that match your unique taste!



To explore the Colab notebook for detailed code implementation and analysis, click [here](https://github.com/virajbhutada/Movie-Recommendation-System/blob/main/Movie%20Recommendation%20System/Movie_Recommendation_System_Colab%20(1).ipynb).



## Key Features

- **User-Friendly Interface**: Intuitive design for easy navigation and interaction.

- **Highly Accurate Recommendations**: Utilizes advanced algorithms to enhance user experience.

- **Wide Variety of Movies**: Access to a diverse range of films across different genres.



## Conclusion



With **Movie Match**, embark on a cinematic journey to explore films that resonate with your preferences. Whether you're in the mood for a classic or seeking the latest blockbusters, Movie Match is here to guide you to your next favorite movie!



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***Enjoy your cinematic journey with Movie Match!***