The Movie Genre Classification project aims to automatically predict the genre of a movie based on its plot summary. Natural Language Processing (NLP) techniques are employed to process and analyze textual data to make accurate genre predictions.
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Data Collection and Preprocessing : The project starts with collecting a dataset of movies along with their plot summaries. The data is preprocessed to remove noise, such as HTML tags, special characters, and stopwords.
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Feature Engineering : Textual data is converted into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings to prepare it for machine learning models.
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Model Selection and Training : Various machine learning models are explored and trained on the processed data to predict movie genres. Commonly used models may include Support Vector Machines (SVM), Random Forest, or Naive Bayes classifiers.
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Model Evaluation : The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Cross-validation techniques may be employed to ensure robustness and generalizability of the models.
Python : The primary programming language for implementing the project. Natural Language Processing (NLP) Libraries : Libraries like NLTK (Natural Language Toolkit) or spaCy are used for text processing and analysis. Scikit-learn : A machine learning library in Python used for building and evaluating predictive models.
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