Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham). In this project, I have used many algorithms to create a model that can classify SMS messages as spam or not spam. Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam'. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.
This project has been broken down in to the following steps: This project has been broken down in to the following steps:
- Data cleaning
- EDA (Exploratory Data Analysis)
- Text Preprocessing
- Model building
- Evaluation
- Improvement
- The code is implemented in Google Colaboratory with the help of Python 3.9
- Libraries used : Numpy,Pandas, Matplotlib,Seaborn and Sklearn.
- For Deploying : Streamlit , Herokuapp