email-spam-detection

Gmail Spam Detection with Logistic Regression

This repository provides a user-friendly web application built with Streamlit to predict whether an email is spam or not. It leverages a pre-trained machine learning model for classification.

Features

Intuitive Interface: Enter your email text in a designated area and receive a prediction with a single click.
Machine Learning Powered: The app utilizes a pre-trained model trained on labeled email data to identify spam characteristics.
Streamlit Integration: Streamlit enables a visually appealing and interactive user experience.
Customizable (Optional): With programming knowledge, you can modify the app’s appearance and functionalities.

Requirements

Python 3.7 or Later: The programming language used to build the app.
Streamlit: A Python library for creating web applications. You can install it using pip install streamlit.
joblib: A Python library for saving and loading machine learning models. Install with pip install joblib.
scikit-learn (if training a model): A Python library for machine learning algorithms. Install with pip install scikit-learn.
NumPy (if training a model): A Python library for numerical computations needed during model training. Install with pip install numpy.

Installing Required Libraries:

Open a terminal or command prompt and run the following command:

pip install streamlit joblib scikit-learn numpy

Usage

Clone the Repository:

Use Git to clone this repository to your local machine:
```
git clone https://github.com/Shreesh-Sree/Email_spam_detection.git
```
Navigate to the Project Directory:

Use the cd command to change directories:
```
cd Email_spam_detection
```
Ensure Model Files Exist (Optional):

The app relies on pre-trained model files (model.pkl and vectorizer.pkl) located in the same directory as the app.py script. These files are crucial for the app to function. If you don’t have them, you’ll need to train your own model (see the “Model Training” section below).
Run the Streamlit App:

In your terminal within the project directory, execute the following command to launch the app:
```
streamlit run app.py
```

This will open the web app in your default web browser, usually at http://localhost:8501.

Using the Web App:

You’ll see a text area labeled “Enter Your Mail”.
Paste or type the email content you want to check for spam.
Click the “Predict” button.
The app will display the prediction below the button, indicating “Spam” (in red) or “Normal Mail” (in green).

Model Training (Optional)

If you want to train a custom model using your own email data, here’s a basic example using scikit-learn:

Understanding the Code (Optional):

pandas: A Python library for data analysis (used for loading and manipulating the email dataset).
train_test_split: A function from scikit-learn to split the data into training and testing sets for model evaluation.
TfidfVectorizer: A scikit-learn tool to convert text data into numerical features suitable for machine learning algorithms.
LogisticRegression: A machine learning algorithm used for classification tasks (spam vs. non-spam in this case).
joblib: Used to save the trained model (model.pkl) and vectorizer (vectorizer.pkl) for future use by the app.

Important Note: This code snippet serves as a basic example. Training a robust model requires careful data preparation, feature engineering, and hyperparameter tuning for optimal performance.

Contributing

I welcome contributions to this project! Feel free to fork the repository, make changes, and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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