Hello, I'm back with another exciting project. I'm hoping that this readme will speak for my work and my enthusiasm for these types of projects.
Welcome to my machine learning project, where I've built a model to classify SMS messages as either "Spam" or "Ham" using a dataset of SMS messages.
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Spam: This term refers to unsolicited bulk messages sent via electronic messaging systems, especially for advertising purposes. Essentially, spam is any message you didn’t explicitly ask for.
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Ham: Now, here’s where it gets interesting! “Ham” is the opposite of spam. It refers to email that is generally desired and isn’t considered spam. In other words, it’s “non-spam” or “good mail.”
you can clone my repository for work or you can download the dataset by clicking on the link below
The dataset used for this project is the SMS Spam Collection Dataset, which can be downloaded from Kaggle.
Dataset link : - https://www.kaggle.com/uciml/sms-spam-collection-dataset
The goal of this project is to develop a machine learning model that can accurately classify SMS messages as either "Spam" or "Ham". The model is trained on a dataset of labeled SMS messages, which allows it to learn patterns and features that distinguish spam from ham messages.
The project involves the following steps:
- Data Preprocessing: Cleaning and preprocessing the dataset to prepare it for training the model.
- Feature Engineering: Extracting relevant features from the SMS messages that can help the model make accurate predictions.
- Model Training: Training a machine learning model on the preprocessed dataset using the extracted features.
- Model Evaluation: Evaluating the performance of the trained model using metrics such as accuracy, precision, and recall.
- Deployment: Deploying the trained model using Flask, a lightweight web framework for Python.
I have used Flask to deploy the trained model, which allows users to input an SMS message and receive a prediction of whether it is spam or ham.
Model.Deployment.mp4
- Python: The primary programming language used for this project.
- Scikit-learn: A machine learning library used for training and evaluating the model.
- Flask: A lightweight web framework used for deploying the model.
- Pandas: A library used for data manipulation and analysis.
There are several ways to improve and extend this project, including:
- Collecting more data: Collecting more SMS messages to improve the accuracy and robustness of the model.
- Experimenting with different models: Trying out different machine learning models and algorithms to see which one performs best.
- Improving the user interface: Improving the user interface of the deployed model to make it more user-friendly and intuitive.
