LINK:- https://ai-rajneesh-email-sms-spam-classifier-rajneesh-app-bx3u7c.streamlit.app/
Building a machine learning model for classifying email and SMS messages as spam or not spam typically involves the following steps:-
Use the test set to evaluate the model's performance on unseen data and ensure that it generalizes well to new examples.
Once the model has been trained and tested, it can be deployed for use in a production environment.
The model performance may degrade over time, so it's important to monitor the model's performance and retrain it as necessary. Also, update the dataset with new data to improve the model's performance.
This machine learning model is designed to classify email and SMS messages as spam or not spam. The model is trained on a dataset of labeled examples, and can be used to classify new, unseen messages.
The training process involves feeding the model a large number of labeled examples, and adjusting the model's internal parameters so that it can correctly classify new, unseen examples. Once the model is trained, it can be used to classify new incoming messages as either email or SMS.
In order to build such a model a dataset of labeled emails and SMS is needed. Then, features are extracted from the texts such as word frequency, word count, punctuation usage, capitalization etc. Then, a classification algorithm is chosen and applied to the features to train the model. Common algorithm are Naive Bayes, Decision Tree, Random Forest etc.
It's important to note that, any model can be improved by fine tuning the model's hyperparameters, which are the settings that control the behavior of the model's training algorithm. Also, test the model on unseen dataset in order to evaluate the model's performance.
To use the model, you will need to provide it with a new email or SMS message as input, and it will output a label indicating whether the message is spam or not.
The performance of the model will depend on a variety of factors, including the quality and quantity of the training data, the complexity of the model, and the specific task and dataset. As a general rule, the more data and the more complex the model, the better the performance will be. But the trade-off is that more data and complexity will also make the model slower and more resource-intensive to run.
DATASET LINK:- kaggle- https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset (spam.csv)
The training data used to build the model consists of a set of labeled examples of email and SMS messages. The specific format and contents of the training data will depend on the model and the dataset used.
#Limitations The model will only work with text based Email/SMS. It may not be able to handle non-textual data like images, audio or video.
It's important to keep in mind that any machine learning model, including this one, is only as good as the data it's trained on. If the training data is biased or not representative of the real-world distribution of data, the model's performance will be impacted accordingly.
Also it's trained on a specific dataset, it may not work as well on completely different types of data or problem statements.