- We start a with data set with X and Y value (X – SMS and Y- Spam or Ham value)
- Do preprocessing
- Do Feature Extraction
- Device data set into two potion (Traning – Have X and Y ) – Train and Test data- Split ratio 80 and 20
- Using Train data , Input to a ML model
- ML model –m Will Learn relation about X and Y
- And We gave only the X to ML model and Ask to predict What is Y.
- Model Predict some Y and We compare Y with the Actual Y
- How many of them are correct?
- Check both get Accuracy = 97 %
In here All details and describe in document.
If you are looking for a GitHub repository description related to spam classification using Natural Language Processing (NLP), I can provide a generic description based on common practices and features that such a repository might contain. However, please note that the following is a fictional example, and you would need to search GitHub for real-world implementations.
Spam Classification using NLP 📚 Overview This repository contains a machine learning project focused on classifying emails as either spam or non-spam (ham) using Natural Language Processing (NLP) techniques. The goal is to develop an accurate and efficient model that can automatically filter out spam emails, thereby improving email security and user experience.
🔍 Features
Data Preprocessing: Includes scripts for cleaning and preprocessing the email text data, including tokenization, stop word removal, and stemming/lemmatization.
Feature Extraction: Utilizes TF-IDF (Term Frequency-Inverse Document Frequency) and/or word embeddings (e.g., Word2Vec, GloVe) to convert the text data into numerical vectors suitable for machine learning algorithms.
Model Training: Implements various machine learning algorithms such as Naïve Bayes, Logistic Regression, Support Vector Machines (SVM), and Random Forests for training and evaluating the spam classification model.
Evaluation Metrics: Computes performance metrics such as accuracy, precision, recall, F1-score, and ROC AUC to evaluate the effectiveness of the trained models.
Deployment: Provides guidance and resources for deploying the trained model into a production environment, including API integration and scalability considerations.
🔧 Requirements
Python 3.x Libraries: scikit-learn, pandas, numpy, nltk, gensim, etc.