Pemrograman Data Science Studi Kasus Klasifikasi Dan Prediksi Hepatitis C Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui
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PEMROGRAMAN DATA SCIENCE: Studi Kasus Klasifikasi dan Prediksi Hepatitis C Menggunakan Scikit-Learn, Keras, dan TensorFlow dengan Python GUI
Dataset yang dipakai pada buku ini berisi nilai-nilai laboratorium dari sejumlah donor darah dan pasien Hepatitis C dan nilai-nilai demografis seperti usia dan lainnya. Dataset diperoleh dari UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/HCV+data. Semua atribut kecuali Category dan Sex adalah numerikal. Atribut 1 sampai 4 mengacu pada data pasien dan atribut 5 sampai 14 mengacu pada data laboratorium: X (Patient ID/No.), Category (diagnosis) (values: '0=Blood Donor', '0s=suspect Blood Donor', '1=Hepatitis', '2=Fibrosis', '3=Cirrhosis'), Age (in years), Sex (f,m), ALB, ALP, ALT, AST, BIL, CHE, CHOL, CREA, GGT, and PROT. Atribut target untuk klasifikasi adalah Category (2): blood donors vs. Hepatitis C (termasuk ('just' Hepatitis C, Fibrosis, Cirrhosis). Selanjutnya, pada buku ini Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk mengklasifikasi dan memprediksi Hepatitis C. Model-model yang digunakan adalah K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, LGBM classifier, XGB classifier, MLP classifier, dan ANN. Terakhir, Anda akan mengembangkan GUI menggunakan Qt Designer dan PyQt5 untuk ROC, distribusi fitur, keutamaan fitur, menampilkan batas-batas keputusan tiap model, diagram nilai-nilai prediksi versus nilai-nilai sebenarnya, matriks confusion, kurva rugi, kurva akurasi, kurva pembelajaran model, skalabilitas model, dan kinerja model.
HEPATITIS C: Classification and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
In this comprehensive project focusing on Hepatitis C classification and prediction, the journey begins with a meticulous exploration of the dataset. Through Python, Scikit-Learn, Keras, and TensorFlow, the project aims to develop an effective model to predict Hepatitis C based on given features. The dataset's attributes are systematically examined, and their distributions are analyzed to uncover insights into potential correlations and patterns. The subsequent step involves categorizing the feature distributions. This phase sheds light on the underlying characteristics of each attribute, facilitating the understanding of their roles in influencing the target variable. This categorization lays the foundation for feature scaling and preprocessing, ensuring that the data is optimized for machine learning. The core of the project revolves around the development of machine learning models. Employing Scikit-Learn, various classification algorithms are applied, including K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, Gradient Boosting, AdaBoost, Light Gradient Boosting, Multi-Layer Perceptron, and XGBoost. The models are fine-tuned using Grid Search to optimize hyperparameters, enhancing their performance and generalization capability. Taking the project a step further, deep learning techniques are harnessed to tackle the Hepatitis C classification challenge. A key component is the construction of an Artificial Neural Network (ANN) using Keras and TensorFlow. This ANN leverages layers of interconnected nodes to learn complex patterns within the data. LSTM, FNN, RNN, DBN, and Autoencoders are also explored, offering a comprehensive understanding of deep learning's versatility. To evaluate the models' performances, an array of metrics are meticulously employed. Metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are meticulously calculated. The significance of each metric is meticulously explained, underpinning the assessment of a model's true predictive power and its potential weaknesses. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. The culmination of the project involves the creation of a user-friendly Graphical User Interface (GUI) using PyQt. The GUI enables users to interact seamlessly with the models, facilitating data input, model selection, and prediction execution. A detailed description of the GUI's components, including buttons, checkboxes, and interactive plots, highlights its role in simplifying the entire classification process. In a comprehensive journey of exploration, experimentation, and analysis, this project effectively marries data science and machine learning. By thoroughly examining the dataset, engineering features, utilizing a diverse range of machine learning models, harnessing the capabilities of deep learning, evaluating performance metrics, and creating an intuitive GUI, the project encapsulates the multi-faceted nature of modern data-driven endeavors.
Analisis dan Prediksi Stroke Menggunakan Scikit-Learn, Keras, dan TensorFlow dengan Python GUI
Menurut Organisasi Kesehatan Dunia (WHO), stroke adalah penyebab kematian ke-2 secara global, yang bertanggung jawab atas sekitar 11% dari total kematian. Dataset yang digunakan pada penelitian ini berguna untuk memprediksi kemungkinan seorang pasien terkena stroke berdasarkan parameter masukan seperti jenis kelamin, usia, berbagai penyakit, dan status merokok. Setiap baris dalam data memberikan informasi yang relevan tentang pasien. Informasi tiap kolom: id: Pengenal unik; gender: "Male", "Female" atau "Other"; age: Usia pasien; hypertension: 0 jika pasien tidak memiliki hipertensi, 1 jika pasien memiliki hipertensi; heart_disease: 0 jika pasien tidak memiliki penyakit jantung, 1 jika pasien memiliki penyakit jantung; ever_married: "No" atau "Yes"; work_type: "children", "Govt_jov", "Never_worked", "Private" atau "Self-employed"; Residence_type: "Rural" atau "Urban"; avg_glucose_level: Rata-rata kadar glukosa dalam darah; bmi: body mass index; smoking_status: "formerly smoked", "never smoked", "smokes" atau "Unknown"*; stroke: 1 jika pasien mengalami stroke atau 0 jika tidak. Selanjutnya, Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk menganalisa dan memprediksi stroke menggunakan dataset yang disediakan di Kaggle. Model-model yang digunakan adalah K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, LGBM classifier, XGB classifier, MLP classifier, dan CNN 1D. Terakhir, Anda akan mengembangkan GUI menggunakan Qt Designer dan PyQt5 untuk ROC, distribusi fitur, keutamaan fitur, menampilkan batas-batas keputusan tiap model, diagram nilai-nilai prediksi versus nilai-nilai sebenarnya, matriks confusion, rugi pelatihan, rugi akurasi, kurva pembelajaran model, skalabilitas model, dan kinerja model.