This project is related to Fake News Analysis using Machine Learning Techniques and Tools.
- Collecting Datasets on Fake News Analysis.
- Building of a dataset from a set of known and well-done datasets.
- Loading and Analyzing dataset.
- Splitting the Dataset into training and testing sets.
- Preprocessing of the text.
- Choose a Learning Model, Methodology or Schema for training the dataset.
- Fitting the Model with proper parameters and Predicting a feasible outcome. Check the Model Accuracy
- Report and Visualization of the predicted outcomes.
- If the results are not that convincing, then Tuning and Optimizing Model with necessary algorithms, is needed.
- Testing the Optimized Model and Reporting its whereabouts and results.
- After Prev.Step, if the obtained results are not still that convincing then "Repeat Prev->Prev.Step" with a more efficient technique.
- Summary Report on the Model.
Before running the notebook on google colab, you may firstly need to upload in your google drive these 03 files {'Fake.csv', 'True.csv', 'News.csv'} in the path that suits you best. Secondly, in the notebook on colab, change the path variable to the specific location where you uploaded the above-mentioned files. (e.g: '/content/drive/My Drive/' or '/content/drive/My Drive/datasets/').