News-Category-Prediction

**Run the python notebook first to create pickle files

Frontend is implemented using FLASK API. Python scripts are called using HTML and Flask API.

Inside Python Notebook: This model predicts news category. I used the concepts of Machine Learning and Natural Language Processing. I compared the performance of two algorithms in correctly classifying the category of the news. I used Multinomial Naive Bayes and Logistic Regression for comparison. For Multinomial NB, I got accuracy of 93.86%. For Logistic Regression, I got accuracy of 94.01%. I split the dataset into 70% train dataset and 30% test dataset.

Tools Used:

1. Python
2. Numpy
3. ScikitLearn libraries
4. Flask API

Method:

1. Import the dataset into the python notebook.
2. Import all the necessary python libraries.
3. Store the 'text' column in X and 'category' column in Y.
4. Split the X and Y in 70% train and 30% test dataset using train_test_split() method.
5. Vectorize X using tfidfVectorizer() and Y using LabelEncoder().
6. Create and test MultinomialNB and Logistic Regression models using X and Y.
7. Now test the created models on test datasets created in step 4.
8. Print the accuracy for both the models.

I have also used pickle in python to store the models trained to reduce the effort of training models every time I run the code.