AshutoshDevpura / Message-spam-classifier-UCI-ML

NOTICE

• Visit the website: https://ashutoshdevpura-message-spam-classifier-uci-ml-app-ltp3nl.streamlit.app/
• Enter the message you want to classify as spam or not.
• Click on the "Check for Spam" button button to get the result.
• You can also use the examples from the "Messages.txt" file by copying and pasting them into the message input field.

Message-spam-classifier-UCI-ML

This project involves building a spam classifier using the Naive Bayes algorithm. The dataset used is a set of SMS messages labeled as spam or ham.

Table of Contents

• Technologies Used
• Dataset Description
• Project Description
• Data Cleaning and EDA
• Data Preprocessing
• Model Building
• Output
• Conclusion

Technologies Used

• Python 3.9.7
• pandas 1.3.3
• scikit-learn 0.24.2
• matplotlib 3.4.3
• seaborn 0.11.2
• nltk 3.6.3

Dataset Description

The dataset used is a collection of SMS messages that are labeled as spam or ham.

Project Description

The main goal of this project is to build a Naive Bayes classifier that can accurately predict whether an SMS message is spam or ham.

Data Cleaning and EDA

The first step in the project involved cleaning the dataset by dropping unnecessary columns and renaming columns for better readability. I also checked for missing values and duplicate values, and removed any duplicates found. Exploratory data analysis was done to understand the distribution of spam and ham messages in the dataset. I used various visualization techniques such as a pie chart and histograms to visualize the distribution of the number of characters, words, and sentences in spam and ham messages.

Data Preprocessing

The next step involved preprocessing the text data to convert it into a format that can be fed into a machine learning model. I performed text normalization techniques such as converting all text to lowercase, removing stopwords and punctuation, and stemming the text. I also created a word cloud to visualize the most common words in spam and ham messages.

Model Building

Finally, I built a Naive Bayes classifier using the CountVectorizer and TfidfVectorizer to convert text into numerical features. I split the dataset into training and testing sets and evaluated the performance of the model using accuracy, confusion matrix, and precision. I used three types of Naive Bayes models: GaussianNB, MultinomialNB, and BernoulliNB. I found that MultinomialNB provided the highest accuracy and precision.

Output

• SPAM

• NOT A SPAM

Conclusion

I was able to build a Naive Bayes classifier that can accurately predict whether an SMS message is spam or ham with an accuracy of approximately 98%. The model can be further improved by using more advanced natural language processing techniques and trying out other classification algorithms.