SMS/EMAIL SPAM CLASSIFIER

Introduction

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.

Dataset

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.

Model

The model is built using the following steps:

Data Preprocessing:

Tokenization, stop word removal, and text vectorization using TF-IDF.

Model Training:

Using machine learning algorithms like Naive Bayes, Logistic Regression, or Support Vector Machines (SVM).

Model Evaluation:

Evaluating the model's performance using accuracy, precision, recall, and F1-score.

User Interface Design:

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> INTERFACE

2> MESSAGE INSERTION

3> CLASSIFIED OUTPUT

Technologies Used

1> Python

2> Streamlit

3> Pandas

4> Matplotlib

5> Seaborn

6> NLP libraries (such as NLTK )