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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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tags: |
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- sklearn |
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- machine learning |
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- movie-genre-prediction |
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- multi-class classification |
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--- |
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## Model Details |
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### Model Description |
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The goal of the competition is to design a predictive model that accurately classifies movies into their respective genres based on their titles and synopses. |
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The model takes in inputs such as movie_name and synopsis as a whole string and outputs the predicted genre of the movie. |
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- **Developed by:** [Shalaka Thorat] |
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- **Shared by:** [Data Driven Science- Movie Genre Prediction Contest: competitions/movie-genre-prediction] |
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- **Language:** [Python] |
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- **Tags:** [Python, NLP, Sklearn, NLTK, Machine Learning, Multi-class Classification, Supervised Learning] |
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### Model Sources |
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- **Repository:** [competitions/movie-genre-prediction] |
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## Training Details |
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We have used Multinomial Naive Bayes Algorithm to work well with Sparse Vectorized data, which consists of movie_name and synopsis. |
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The output of the model is a class (out of 10 classes) of the genre. |
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### Training Data |
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All the Training and Test Data can be found here: |
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[competitions/movie-genre-prediction] |
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#### Preprocessing |
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1) Label Encoding |
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2) Tokenization |
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3) TF-IDF Vectorization |
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4) Preprocessing of digits, special characters, symbols, extra spaces and stop words from textual data |
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## Evaluation |
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The evaluation metric used is [Accuracy] as specified in the competition. |
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