CODSOFT-Spam-SMS-Detection-Using-ML

Spam SMS Detection using Machine Learning is a text classification project that identifies whether an SMS is spam or not. It uses NLP techniques for text preprocessing and a machine learning model to accurately detect unwanted messages.

Check out my application on SMS Spam Detection

https://sms-spam-detection-pfr0xnyizaf.streamlit.app/

SMS Spam Detection

This project aims to develop a machine-learning model to detect spam messages in SMS text data. It utilizes natural language processing (NLP) techniques and a supervised learning algorithm to classify SMS messages as either spam or non-spam (ham).

Dataset

The dataset used for this project is the "SMS Spam Collection" from the UCI Machine Learning Repository. It contains a collection of 5,574 SMS messages, labeled as spam or ham. The dataset can be downloaded from [link to dataset] (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection).

The dataset file (sms_spam_dataset.csv) contains two columns:

label: Indicates whether the message is spam (1) or ham (0).
text: The actual text content of the SMS message.

Requirements

To run the project, you need the following dependencies:

Python 3.x
pandas
numpy
scikit-learn
nltk (Natural Language Toolkit)
matplotlib
You can install the required packages by running the following command:

pip install pandas numpy scikit-learn nltk matplotlib

Usage

Clone the repository or download the project files.

Place the sms_spam_dataset.csv file in the project directory.

Run the sms_spam_detection.py script to train and evaluate the spam detection model.

The script will load the dataset, preprocess the text data, and train a machine learning model using the TF-IDF (Term Frequency-Inverse Document Frequency) technique.

After training, the model will be evaluated on a holdout set and the performance metrics (such as accuracy, precision, recall, and F1-score) will be displayed.

Finally, you can use the trained model to predict the label (spam/ham) of new SMS messages by modifying the predict function in the script.

Results

The trained model achieved an accuracy of 97.10 % and Precision is 100 % on the test set and performed well in terms of precision, recall, and F1-score.

Metric Score
Accuracy 97.10 %
Precision 100 %
Recall 76.19 %
F1-score 86.49 %
Feel free to contribute, modify, or use the code according to the terms of the license.