{"id":28996656,"url":"https://github.com/shubhamgoyal575/spam_detective","last_synced_at":"2025-07-02T22:38:02.449Z","repository":{"id":273328806,"uuid":"919340094","full_name":"shubhamgoyal575/Spam_Detective","owner":"shubhamgoyal575","description":"This project uses machine learning to classify messages as spam or ham based on text analysis. It includes data preprocessing, feature extraction (TF-IDF), and classification models like Logistic Regression and Naive Bayes for accurate spam detection. 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This repository contains an end-to-end implementation of a machine learning model that predicts whether a given message is spam or ham. The project uses Natural Language Processing (NLP) techniques to process and classify text data, with predictions performed using a trained machine learning model.\n\n## 📖 Project Overview\nThe goal of this project is to classify messages into two categories:\n\n- **Spam:** Unwanted or unsolicited messages, often promotional or fraudulent.\n- **Ham:** Genuine, non-spam messages.\nThis project demonstrates the use of NLP techniques and machine learning to build a robust and accurate spam classifier.\n\n## 🛠️ Tools and Technologies Used\n**Programming Language:** Python\n\n**Libraries:**\n- numpy and pandas for data manipulation\n- scikit-learn for building and evaluating the machine learning model\n- nltk and re for text preprocessing\n- matplotlib and seaborn for data visualization\n- Environment: Jupyter Notebook\n\n## 🧑‍💻 Key Steps in the Project\n### Data Collection:\nThe dataset contains labeled text messages, where each message is marked as either \"spam\" or \"ham.\"\n\n### Data Preprocessing:\n- Removing unnecessary characters, punctuation, and stopwords.\n- Tokenizing the text into individual words.\n- Converting words into their base form using lemmatization or stemming.\n\n### Feature Extraction:\nUsing Count Vectorizer , TF-IDF Vectorizer to convert text data into numerical form suitable for machine learning models.\n\n### Model Selection and Training:\nTrained various machine learning models, including:\n- Naive Bayes Classifier\n- Logistic Regression\n- Support Vector Machine (SVM)\n\nSelected the best-performing model based on accuracy, precision, recall, and F1-score.\n\n### Model Evaluation:\nEvaluated the model on a test dataset.\nVisualized performance metrics such as confusion matrix, ROC curve, and classification report.\n\n### Prediction:\nBuilt a function to predict whether a new message is spam or ham using the trained model.\n\n📊 Results\nAccuracy: Achieved an accuracy of 98% on the test dataset (update with your result).\n- Precision: 100%\nThe model demonstrated strong performance in distinguishing between spam and ham messages.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamgoyal575%2Fspam_detective","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshubhamgoyal575%2Fspam_detective","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshubhamgoyal575%2Fspam_detective/lists"}