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cnn_base_and_tuned.ipynb - Contains the python code for the cnn baseline and the cnn tuned model.
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data_collection_and_data_cleaning.ipynb - Python script for thr data cleaning.
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data_visualisation.ipynb - This file have python code for the data visualisation.
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gru_base_and_tuned.ipynb - Contains the python code for the gru baseline and the gru tuned model.
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lstm_base_and_tuned.ipynb - Both the lstm baseline and the lstm tuned model's python source code is present in this file.
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mlp_base_and_tuned.ipynb - Contains the python code for the mlp baseline and the mlp tuned model.
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naivebayes_base_and_tuned.ipynb - Contains the python code for the naive bayes baseline and the naive bayes tuned model.
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random_forest_base_and_tuned.ipynb - Includes the python code for the baseline random forest model and the tuned random forest model.
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report.pdf - Project report.
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stochastic_gradient_descent_base_and_tuned.ipynb - Contains the python code for the stochastic gradient descent baseline and the stochastic gradient descent tuned model.
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xgboost_base_and_tuned.ipynb - Contains the python code for the xgboost baseline and the xgboost tuned model.
The google colab link is provided below:
https://drive.google.com/drive/folders/1Z4AehVm79Z9NSb60YFCgQhLgUQ-DNUrD?usp=sharing
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Enter the google colab link and the download the seven datasets which are “business.csv”, “food.csv”, “health.csv”, “entertainment.csv”, “environment.csv”, “sports.csv”, “politics.csv”
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Run the file “data_collection_and_data_cleaning.ipynb” on jupyter notebook.
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Upload the file in to the colab and run file “data_visualisation.ipynb”
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Now run the file “naivebayes_base_and_tuned.ipynb”
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Now run the file “random_forest_base_and_tuned.ipynb”
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Now run the file “stochastic_gradient_descent_base_and_tuned.ipynb”
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Now run the file “xgboost_base_and_tuned.ipynb”
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Now run the file “cnn_base_and_tuned.ipynb”
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Now run the file “lstm_base_and_tuned.ipynb”
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Now run the file “gru_base_and_tuned.ipynb”
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Now run the file “mlp_base_and_tuned.ipynb”