The SMS Spam Classifier project aims to build a machine learning model to identify and classify SMS messages as spam or ham (non-spam). This project demonstrates the use of natural language processing (NLP) techniques to preprocess text data and train a classification model.
The dataset used in this project is the SMS Spam Collection Data Set. It contains a collection of SMS messages labeled as spam or ham.
The model is built using the following steps:
Tokenization, stop word removal, and text vectorization using TF-IDF.
Using machine learning algorithms like Naive Bayes, Logistic Regression, or Support Vector Machines (SVM).
Evaluating the model's performance using accuracy, precision, recall, and F1-score.
Enhanced my ability to design intuitive and user-friendly interfaces that improve the overall user experience.
Techniques used
1>Used various classifiaction models like NAIVE BAYES , LOGISTIC REFRESSION , SVC , XGBOOST etc.
2>Removing Stop Words
4>TOKENIZATION
3>using NLTK library Tfidf for text vectorization
1> Python
2> Streamlit
3> Pandas
4> Matplotlib
5> Seaborn
6> NLP libraries (such as NLTK )