https://github.com/hiteshydv001/spam-classifier-using-machine-codeclause-learning
SMS spam classifier
https://github.com/hiteshydv001/spam-classifier-using-machine-codeclause-learning
Last synced: about 1 month ago
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SMS spam classifier
- Host: GitHub
- URL: https://github.com/hiteshydv001/spam-classifier-using-machine-codeclause-learning
- Owner: Hiteshydv001
- Created: 2023-09-27T21:07:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-02T13:56:25.000Z (over 1 year ago)
- Last Synced: 2025-02-13T03:44:22.454Z (3 months ago)
- Language: Jupyter Notebook
- Size: 18.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Spam Classifier using Machine learning
This project is a Spam Classifier implemented using machine learning techniques to classify text messages or emails as either spam (unwanted or malicious) or not spam (ham). The classifier helps filter out unwanted content and improve communication efficiency.
Spam classification is a common application of natural language processing (NLP) and machine learning. This project showcases how to build, train, and evaluate a spam classifier using Python and popular machine learning libraries.
## Dataset:-
The spam classifier was trained on a labeled dataset of text messages or emails, containing examples of both spam and ham messages. The dataset is diverse and representative of real-world spam patterns.
## Demo
## Roadmap:-
**Spam Classifier Using Machine Learning: Major Steps**
**Data Collection:**
- Gather a labeled dataset of emails or text messages, where each message is tagged as spam or not spam (ham).
- Ensure the dataset is representative and diverse.
**Data Preprocessing:**
- Text Cleaning: Remove HTML tags, punctuation, special characters, and irrelevant formatting.
- Tokenization: Split text into words or tokens.
- Lowercasing: Convert all text to lowercase for consistency.
- Stopword Removal: Eliminate common words (e.g., "and," "the," "in") that don't carry significant information.
- Lemmatization or Stemming: Reduce words to their base or root form.
**Feature Extraction:**
- Convert text data into numerical features that machine learning algorithms can work with. Common techniques include:
- TF-IDF (Term Frequency-Inverse Document Frequency)
- Word Embeddings (e.g., Word2Vec, GloVe)
- Bag of Words (BoW)
**Data Splitting:**
- Split the preprocessed dataset into training and test sets (e.g., 70% for training, 30% for testing) to evaluate model performance.
**Model Selection:**
- Choose an appropriate machine learning algorithm for text classification.
**Common choices include:**
- Naive Bayes
- Support Vector Machines (SVM)
- Logistic Regression
- Random Forest
- Neural Networks (e.g., LSTM, CNN)
**Model Training:**
- Train the selected model on the training data using the extracted features.
- Fine-tune model hyperparameters to optimize performance.
**Model Evaluation:**
- Evaluate the trained model on the test dataset using metrics such as accuracy, precision, recall, F1-score, and ROC AUC.
- Check for false positives and false negatives, as they have different implications for spam detection.
**Model Interpretability (Optional):**
- If applicable, interpret the model's decisions and feature importance.
- Explain why certain messages are classified as spam.
**Deployment (Optional):**
- Deploy the trained model as a spam filter in an email system or messaging platform.
- Implement an interface for users to submit text for classification.
## Tech Stack
**Languages:** Python
**Framework:** Jupyter Notebook || Pycharm
## 🔗 Links
## let's connect
[](https://www.linkedin.com/)