🧠 ANN Classification

This project demonstrates a simple Artificial Neural Network (ANN) for classification tasks using Keras and TensorFlow. It is designed to classify structured data into multiple or binary classes depending on the dataset used.

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📚 Overview

This neural network is built with fully connected layers using Keras's Sequential API. It includes data preprocessing, model training, evaluation, and visualization.

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🛠️ Project Structure

ANN-Classification-/ │ ├── ann_classification.ipynb # Jupyter Notebook with full pipeline ├── dataset.csv # Input dataset (placeholder name) ├── model.h5 # Saved ANN model ├── requirements.txt # Python dependencies └── README.md # Project documentation

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🚀 Features

• Build and train an Artificial Neural Network from scratch
• Preprocess tabular data using normalization/encoding
• Evaluate model performance using accuracy and confusion matrix
• Save and load trained models

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📊 Dataset

Replace this with the name of the dataset used (e.g., Iris, Banknote Authentication, etc.)

• Contains numerical and/or categorical features
• Target column represents class label(s)

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🧠 ANN Architecture

• Input Layer: Depends on the number of features
• Hidden Layers: Dense layers with ReLU activation
• Output Layer:
  • Sigmoid (for binary classification)
  • Softmax (for multi-class classification)
• Loss Function:
  • binary_crossentropy for binary classification
  • categorical_crossentropy for multi-class
• Optimizer: Adam

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📈 Evaluation

• Accuracy
• Confusion Matrix
• Loss and Accuracy curves (training vs validation)

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