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SMS Spam Detection Machine Learning Project which predicts whether the message is a spam or not. SMS Spam Collection dataset from Kaggle was used to classify the messages into 2 classes- Ham(1) and Spam(0) using stemming, Bag of Words model and Naive Bayes Classifiers.
Note:The dataset is an unbalanced dataset and therefore, for this situation the role of Precision becomes quite important.Precision is more focused in the positive class than in the negative class, it actually measures the probability of correct detection of positive values,
Consider the following case scenario -'suppose if the message is not a spam and if it's been predicted by the model as a spam, the consumer is going to miss that message.' So, for this type of unbalanced dataset, precision defined as {TP/(TP+FP)} plays an important role along with accuracy_score. My objective was to reduce the FP(False Positive) value as much as possible for this case and in order to overcome this issue, Naive Bayes classifiers namely, MultinomiallNB and BernoulliNB were implemented to get best accuracy_score and precision_score from the dataset.
The code is written in Python 3.6.10. If you don't have Python installed, you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after downloading the code:
About Datasets :
The original dataset can be found here. The creators would like to note that in case you find the dataset useful, please make a reference to previous paper and the web page: http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/ in your papers, research, etc.
We offer a comprehensive study of this corpus in the following paper. This work presents a number of statistics, studies and baseline results for several machine learning methods.
Almeida, T.A., Gómez Hidalgo, J.M., Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11), Mountain View, CA, USA, 2011.
Running the web app
Locally
- Install requirements
pip install -r requirements.txt --user - Run flask web app
python app.py
