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TV Show Search Platform

The TV Show Search Platform is a powerful tool designed to facilitate the search for TV shows based on keywords. Leveraging the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, this platform extracts relevant information from TV show subtitles, making it easier for users to find shows matching their interests.

Features

  • Keyword Search: Users can input keywords to find TV shows related to specific topics or themes.

  • TF-IDF Algorithm: The platform utilizes the TF-IDF algorithm to calculate the importance of keywords within the TV show subtitles. This allows for efficient and accurate matching.

  • Subtitle Analysis: The platform extracts and analyzes words from TV show subtitles to enhance the search functionality.

How It Works

  • Keyword Input: Users enter keywords related to the TV shows they are looking for.

  • TF-IDF Matching: The platform applies the TF-IDF algorithm to identify TV shows that best match the provided keywords. This ensures that shows with a higher relevance to the input keywords are ranked higher in the search results.

  • Subtitle Extraction: The platform extracts and analyzes words from TV show subtitles to strengthen the matching process. This enables a more comprehensive understanding of the show's content.

  • Search Results: Users receive a list of TV shows ranked according to their relevance to the input keywords. This makes it easier for users to discover shows that align with their interests.

What I Learned

Building the TV Show Search Platform involved tackling substantial amounts of data and extracting relevant information efficiently. Here are some key learnings from the project:

  • Data Cleaning with Python: I gained proficiency in using Python for cleaning and processing large datasets. This involved removing irrelevant information and extracting key details needed for the TF-IDF algorithm.

  • TF-IDF Implementation: I learned how to implement the TF-IDF algorithm to calculate the importance of keywords within a collection of TV show subtitles. This algorithm played a crucial role in determining the relevance of shows to user-inputted keywords.

  • Subtitle Analysis Techniques: Extracting meaningful information from TV show subtitles required the development of effective analysis techniques. This involved understanding natural language processing concepts to enhance the accuracy of keyword matching.

  • Efficient Data Extraction: Dealing with a large amount of data required the development of efficient data extraction processes. This involved selecting and extracting relevant information to optimize the search functionality.

Screenshots

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Contributing

Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue on the GitHub repository.

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