This project aims to predict movie ratings using Convolutional Neural Networks (CNN). The system takes movie posters as input and predicts the ratings based on the visual features extracted by the CNN model.
The Movie Rating Prediction System is built using deep learning techniques, particularly Convolutional Neural Networks (CNN), to predict the rating of a movie based solely on its poster. With the increasing importance of visual content in today's media consumption, predicting movie ratings solely from posters can be a valuable tool for various applications such as content recommendation and movie marketing.
The dataset used in this project consists of movie posters along with their corresponding ratings. The dataset is sourced from [insert dataset source]. It contains a diverse collection of movie posters spanning various genres and rating categories.
- Python 3.x
- TensorFlow
- Keras
- Matplotlib
- NumPy
- Pandas
Install the required dependencies using the following command:
pip install -r requirements.txt-
Data Preparation: Before training the model, ensure that the dataset is properly formatted and preprocessed. Use the provided scripts or preprocess the data according to your requirements.
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Model Training: Train the CNN model using the preprocessed dataset. You can adjust hyperparameters and network architecture according to your needs.
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Evaluation: Evaluate the trained model using evaluation metrics such as accuracy, precision, recall, and F1-score. Additionally, visualize the performance using appropriate plots.
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Prediction: Use the trained model to predict ratings for new movie posters. Provide the path to the poster image as input to the model for prediction.
Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to customize this README according to your project's specific details and requirements!