{"id":15704431,"url":"https://github.com/ksdkamesh99/spam-classifier","last_synced_at":"2025-05-12T14:57:36.650Z","repository":{"id":55510491,"uuid":"267117410","full_name":"ksdkamesh99/Spam-Classifier","owner":"ksdkamesh99","description":"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. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Spam-Classifier\n[![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)](https://forthebadge.com)\n[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://forthebadge.com)[![forthebadge](https://forthebadge.com/images/badges/its-not-a-lie-if-you-believe-it.svg)](https://forthebadge.com)\n[![forthebadge](https://forthebadge.com/images/badges/built-by-developers.svg)](https://forthebadge.com)\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ksdkamesh99/Spam-Classifier\"\u003e\n    \u003cimg src=\"images/front.jfif\" alt=\"Logo\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n## 📌 Introduction:-\n\nA 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.\nIt is implemented using LSTM and Word Embeddings to gain accuracy of 97.84%.\n\n## ✔❌Accuracy ❌✔:-\n| Text Preprocessing Type              | Logistic Regression | Multinomial NB | Support Vector Machine  | Decision Tree |\n|--------------------------------------|---------------------|----------------|-------------------------|---------------|\n| TFIDF Vectorizer + PorterStemmer     | 96.68%              | 97.30%         | 98.47%                  | 96.68%        |\n| CountVectorizer + PorterStemmer      | 98.65%              | 98.56%         | 98.74%                  | 97.84%        |\n| CountVectorizer + WordnetLemmatizer  | 98.56%              | 98.29%         | 98.38%                  | 97.75%        |\n| TFIDF Vectorizer + WordnetLemmatizer | 96.41%              | 97.48%         | 98.47%                  | 96.86%        |\n\n\n## WorkFlow:-\n![Workflow of SMS spam Classifer](workflow.gif)\n\n## 🏁 Datasets Used:-\n* The dataset used is SMS Spam Dataset created by UCI Machine Learning.This dataset is downloaded in kaggle.You can download it [here](https://www.kaggle.com/uciml/sms-spam-collection-dataset/download).\n* Reference for this dataset can be found [here](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/)\n## 📧Contact:-\nFor any kind of suggesstions/ help in models code Please mail me at ksdkamesh99@gmail.com.\n\n## 📜 LICENSE\n[MIT](https://github.com/ksdkamesh99/Spam-Classifier/blob/master/LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksdkamesh99%2Fspam-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fksdkamesh99%2Fspam-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksdkamesh99%2Fspam-classifier/lists"}