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.DS_Store (8196, 2019-04-13)
.ipynb_checkpoints (0, 2019-04-13)
.ipynb_checkpoints\News Classification-checkpoint.ipynb (34143, 2019-04-13)
Averages of Three.png (57853, 2019-04-13)
Multinomial Naive Bayes.png (64894, 2019-04-13)
News Classification.ipynb (70463, 2019-04-13)
SVM.png (64964, 2019-04-13)
count_vector.pkl (423209, 2019-04-13)
data (0, 2019-04-13)
data\.DS_Store (8196, 2019-04-13)
data\business (0, 2019-04-13)
data\business\0.txt (360, 2019-04-13)
data\business\1.txt (269, 2019-04-13)
data\business\10.txt (303, 2019-04-13)
data\business\100.txt (268, 2019-04-13)
data\business\101.txt (385, 2019-04-13)
data\business\102.txt (343, 2019-04-13)
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# Classification of News HeadLines ##### [DEMO VIDEO](https://www.youtube.com/watch?v=HeKchZ1dauM&feature=youtu.be) News Headline Classification through multiple machine learning model and comparison of results. Models implemented: * Multinomial Naive Bayes * Support Vector Machines * Neural Network with Softmax Layer Metrics used to evaluate the performance of models: * Precision * Recall * F1 Score We evaluate each classifier's ability to select the appropriate category given an article’s title and a brief article description. The confusion matrix is created to explore the results and calculate the metrics. ###### Feature Extraction Techniques: The collection of text documents is converted to a matrix of token counts using count vectorize that produces a sparse representation of the counts. TFIDF,term frequency–inverse document frequency, is the statistic that is intended to reflect how important a word is to a document in our corpus. This is used to extract the most meaningful words in the Corpus. ###### Link to Dataset: [News Article Dataset](http://acube.di.unipi.it/tmn-dataset/) TagMyNews Datasets is a collection of datasets of short text fragments that we used for the evaluation of our topic-based text classifier. This is a dataset of ~32K english news extracted from RSS feeds of popular newspaper websites (nyt.com, usatoday.com, reuters.com). Categories are: Sport, Business, U.S., Health, Sci&Tech, World and Entertainment. Packages required: * Pandas * sklearn * Numpy    

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