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https://github.com/harsh0713/sms-spam-classification
The "SMS Spam Classification" project aims to develop a machine learning model to automatically identify and classify SMS messages as either spam or legitimate (ham).
https://github.com/harsh0713/sms-spam-classification
bernoulli gaussian-naive-bayes jupyter-notebook multinomial-naive-bayes nltk-python punkt python sklearn-library stopwords streamlit string
Last synced: 1 day ago
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The "SMS Spam Classification" project aims to develop a machine learning model to automatically identify and classify SMS messages as either spam or legitimate (ham).
- Host: GitHub
- URL: https://github.com/harsh0713/sms-spam-classification
- Owner: Harsh0713
- Created: 2024-09-09T08:33:56.000Z (18 days ago)
- Default Branch: main
- Last Pushed: 2024-09-09T12:15:37.000Z (18 days ago)
- Last Synced: 2024-09-26T03:24:09.319Z (1 day ago)
- Topics: bernoulli, gaussian-naive-bayes, jupyter-notebook, multinomial-naive-bayes, nltk-python, punkt, python, sklearn-library, stopwords, streamlit, string
- Language: Jupyter Notebook
- Homepage:
- Size: 813 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Introduction
In today's digital era, the widespread use of mobile communication has led to an increase in unsolicited and malicious messages, commonly referred to as spam. These spam messages can range from harmless advertisements to fraudulent schemes, posing a significant threat to users. To address this issue, the "SMS Spam Classification" project aims to build a robust machine learning model capable of distinguishing between legitimate (ham) messages and spam.
# Thoughts
The project is simple, we work on some dataset and try to find out what are the trends and what is is the pattern, after finding out the pattern one can get idea of what I've uploaded using the dataset.
Also you explore various dataset on kaggle because this one I've downloaded from there.
We first do EDA and I have written comments for everything in the jupyter notebook you can refer it.
After that we checked for various algorithms which gave accuracy and precison score and which was having great score we used that.
Then we trained the mode and pickled the model and used it.
For the website part I've used streamlit.
# Setup
Step1: Download the zip file of the project.
Step2: Open PyCharm and create new project and name it.
Step3: After the project is created copy the files - app.py, model.pkl, vectorizer.pkl and paste it in the PyCharm project.
Step4: Go in the PyCharm terminal and install Streamlit Python framework - pip install streamlit
Step5: Also install nltk by 'pip install nltk' and sklearn by 'pip install scikit-learn'
Step6: Atlast write "streamlit run app.py" on the terminal the project will be implemented.
# Output
