Amazing-Python-Scripts

Description: The primary goal of this project is to develop a machine learning model that can automatically classify emails as either spam or legitimate (ham) based on their content. The LSTM model(Long Short-Term Memory), recurrent neural network (RNN) architecture, is employed to capture sequential patterns in the text data, making it well-suited for natural language processing tasks like this.

Dataset

The project employs a labeled dataset of emails, containing both spam and ham samples. The dataset is preprocessed to tokenize and pad the text data before feeding it into the LSTM model. Please replace 'spam_ham_dataset.csv' in the code with your actual dataset path.

Model Architecture

The LSTM model architecture involves the following key components:

• Embedding Layer: Converts the integer-encoded vocabulary into dense vectors of fixed size.
• First LSTM Layer: Captures sequential patterns by returning sequences instead of a single output.
• Dropout Layer: Helps prevent overfitting by randomly deactivating a fraction of input units during training.
• Second LSTM Layer: Aggregates the output of the previous LSTM layer.
• Dense Layer: Produces a single output unit with sigmoid activation for binary classification.

The model is trained using binary cross-entropy loss and the Adam optimizer.

Install required packages: pip install numpy pandas tensorflow