Spam-Classifier

📌 Introduction:-

A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.84%.

✔❌Accuracy ❌✔:-

Text Preprocessing Type	Logistic Regression	Multinomial NB	Support Vector Machine	Decision Tree
TFIDF Vectorizer + PorterStemmer	96.68%	97.30%	98.47%	96.68%
CountVectorizer + PorterStemmer	98.65%	98.56%	98.74%	97.84%
CountVectorizer + WordnetLemmatizer	98.56%	98.29%	98.38%	97.75%
TFIDF Vectorizer + WordnetLemmatizer	96.41%	97.48%	98.47%	96.86%

WorkFlow:-

🏁 Datasets Used:-

• The dataset used is SMS Spam Dataset created by UCI Machine Learning.This dataset is downloaded in kaggle.You can download it here.

📧Contact:-

For any kind of suggesstions/ help in models code Please tell me.