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https://github.com/lord3008/spam-sms-detection

This is a spam-ham classifier made by me using machine learning.
https://github.com/lord3008/spam-sms-detection

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This is a spam-ham classifier made by me using machine learning.

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README 

        
A spam SMS classification project involves building a machine learning model to automatically identify and filter out unwanted spam messages from legitimate ones. This project typically includes the following steps:



### Key Steps:

1. **Data Collection**: Gather a dataset containing SMS messages labeled as 'spam' or 'ham' (non-spam). Popular datasets include the SMS Spam Collection dataset.



2. **Data Preprocessing**:

   - **Text Cleaning**: Remove unwanted characters, stop words, and punctuation.

   - **Tokenization**: Split messages into individual words or tokens.

   - **Normalization**: Convert all text to lowercase and possibly lemmatize or stem words.

   - **Vectorization**: Convert text data into numerical format using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings.



3. **Exploratory Data Analysis (EDA)**: Analyze the dataset to understand the distribution of spam and ham messages, common words in each category, message lengths, etc.



4. **Model Selection**: Choose a machine learning model suitable for text classification. Common models include:

   - **Naive Bayes**: Often used for text classification due to its simplicity and effectiveness.

   - **Logistic Regression**: A linear model that can perform well with proper feature extraction.

   - **Support Vector Machines (SVM)**: Effective in high-dimensional spaces.

   - **Random Forest**: An ensemble method that can handle a variety of data patterns.

   - **Deep Learning Models**: Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks for more complex patterns.



5. **Model Training**: Train the selected model on the preprocessed dataset.



6. **Model Evaluation**: Evaluate the model's performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Cross-validation techniques can also be applied for a more robust evaluation.



7. **Hyperparameter Tuning**: Optimize the model's hyperparameters to improve performance.



8. **Deployment**: Implement the model in a real-world application to filter incoming SMS messages.



### Basic Example (Using Naive Bayes in Python):

```python

import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import MultinomialNB

from sklearn.metrics import accuracy_score, classification_report



# Load dataset

df = pd.read_csv('sms_spam.csv')  # Example CSV file



# Preprocessing

tfidf = TfidfVectorizer(stop_words='english')

X = tfidf.fit_transform(df['message'])

y = df['label']



# Train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)



# Model training

model = MultinomialNB()

model.fit(X_train, y_train)



# Model evaluation

y_pred = model.predict(X_test)

print(accuracy_score(y_test, y_pred))

print(classification_report(y_test, y_pred))

```



### Applications:

- **Spam Filtering**: Automatically filtering spam messages in messaging applications.

- **Email Classification**: Extending the same principles to classify email as spam or non-spam.

- **Customer Support**: Automatically routing messages to appropriate departments based on content.



A spam SMS classification project demonstrates the practical application of natural language processing (NLP) and machine learning techniques to improve communication efficiency and security.