Python Coding Assignment: Artificial Neural Network

This assignment aims to implement an artificial neural network (ANN) model using TensorFlow or Keras and evaluate its performance on a chosen dataset. Students are encouraged to explore different architectures activation functions, and optimization algorithms.

Artificial Neural Network

Objective:

• Implement an ANN model using TensorFlow or Keras.

• Evaluate the model's performance on a chosen classification or regression dataset.

• Students are encouraged to experiment with various architectures, activation functions, and optimization algorithms.

Tasks:

Dataset Selection: 

Students need to choose a publicly available dataset suited for classification or regression tasks. Common options include MNIST (handwritten digits), Iris flower classification, or Boston housing price prediction.

Data Preprocessing:

• Load the chosen dataset.

• Perform necessary cleaning and preparation steps like normalization or standardization of features.

• Split the data into training, validation, and testing sets.

Model Building:

• Import essential libraries (TensorFlow, Keras, etc.).

• Define the model architecture (number of layers, neurons per layer, activation functions, chosen optimizer).

• Compile the model by specifying the loss function and optimizer.

Model Training:

• Train the model on the training data for a set number of epochs and with a specific batch size.

• Monitor the training process by observing the loss and accuracy (or other relevant metrics) on the validation set to avoid overfitting.

Model Evaluation:

• Evaluate the trained model on the unseen testing set to assess its generalizability.

• Calculate performance metrics like accuracy, precision, recall, F1-score (for classification) or mean squared error, R-squared (for regression).

Report and Discussion:

• Summarize the chosen dataset, preprocessing steps, model architecture, training process, and evaluation results.

• Analyze the model's performance, including any challenges encountered and potential areas for improvement.

• Consider exploring different hyperparameter settings, activation functions, or architectures to potentially enhance performance.

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