• Designed a web app that predicts if the user message is spam or not.
• Scraped the data from kaggle.
• Train different classification algorithms to get the best model.
• Deployed the machne learning model using streamlit library on heroku.

• Streamlit Library: https://www.streamlit.io/
• heroku Platform: https://www.heroku.com/
• Dataset Link: https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
• Packages: pandas, numpy, matplotlib, seaborn, sklearn, nltk, wordcloud, pickle, streamlit

We go through all the features one by one and keep adding new features. I have made the following changes and created new variables:

• Target - Rename the v1 as column target it means if 0 : not spam and 1 : spam.
• Text - Rename the column v2 as Text.
• num_characters - Made new column to check how many number of characters are there in text.
• num_words - Made new column to check how many number of words are there in text.
• num_sentence - Made new column to check how many number of sentence are there in text.

• Traditional Method Used scikit-learn library for the Machine Learning tasks. Applied label encoding and converted the categorical variables into numerical ones.Then I splited the data into training and test sets with a test size of 20%. I tried different classification models( Logistic Regression, Random Forest Classifier, AdaBoostClassifier) and evaluated them using Mean Absolute Error.

• I have deployed the model using Streamlit library on Heroku which is a Platform As A Service(PAAS).
• web application : https://sms-spam-detection-yg.herokuapp.com/