banknote-counterfeit-classification's Introduction

banknote-counterfeit-classification

The main objective is to create a classifier capable of accurately identifying counterfeit banknotes based on a dataset derived from images taken from both genuine and counterfeit banknotes using an industrial camera.
The dataset utilizes Wavelet Transformation (WT) to extract features from these images, including variance, skewness, kurtosis, and entropy, which serve as the basis for classification.
The assignment covers essential steps like data splitting into training and testing sets, k-fold cross-validation for parameter tuning, and performance evaluation using accuracy, ROC curve, and AUC metrics.

Setup: Import the necessary libraries for the project, focusing on sklearn for its comprehensive tools in model training, validation, and metrics evaluation.
Data Loading: Two datasets are loaded - one for training the model (df) and another as a held-out test set (df_eval) for final model evaluation.
Preprocessing: The provided instructions likely include preprocessing steps to clean and prepare the data for modeling, aligning the treatment of both training and evaluation datasets.
Model Training: Utilize the Decision Tree classifier for the task, with a focus on understanding and applying the right preprocessing and feature extraction techniques.
Model Validation and Tuning: Apply k-fold cross-validation to tune the classifier's parameters, ensuring the model's generalizability and robustness.
Performance Evaluation: After training, evaluate the model's accuracy on the test set and use ROC and AUC metrics for a comprehensive assessment of its classification capability.

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