## Coding Challenge - Deep Learning for NLP (Foong)

### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.

Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv

### Write-up:
The accuracy of the model is 0.995. 

There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation
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region:us
TAGS
#region-us 

## Coding Challenge - Deep Learning for NLP (Foong)

### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.

Dataset: URL
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv

### Write-up:
The accuracy of the model is 0.995. 

There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation
## Coding Challenge - Deep Learning for NLP (Foong) ### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.

Dataset: URL
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv ### Write-up:
The accuracy of the model is 0.995. 

There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation
TAGS
#region-us 
 ## Coding Challenge - Deep Learning for NLP (Foong) ### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.

Dataset: URL
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv ### Write-up:
The accuracy of the model is 0.995. 

There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation
passage: TAGS
#region-us 
## Coding Challenge - Deep Learning for NLP (Foong)### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.

Dataset: URL
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv### Write-up:
The accuracy of the model is 0.995. 

There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation