Brain Tumor Analysis Classification And Detection Using Machine Learning And Deep Learning With Python Gui
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BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this book, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. this dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). it also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. In this project, various methods and functionalities related to machine learning and deep learning are covered. Here is a summary of the process: Data Preprocessing: Loaded and preprocessed the dataset using various techniques such as feature scaling, encoding categorical variables, and splitting the dataset into training and testing sets.; Feature Selection: Implemented feature selection techniques such as SelectKBest, Recursive Feature Elimination, and Principal Component Analysis to select the most relevant features for the model.; Model Training and Evaluation: Trained and evaluated multiple machine learning models such as Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, and Support Vector Machines using cross-validation and hyperparameter tuning. Implemented ensemble methods like Voting Classifier and Stacking Classifier to combine the predictions of multiple models. Calculated evaluation metrics such as accuracy, precision, recall, F1-score, and mean squared error for each model. Visualized the predictions and confusion matrix for the models using plotting techniques.; Deep Learning Model Building and Training: Built deep learning models using architectures such as MobileNet and ResNet50 for image classification tasks. Compiled and trained the models using appropriate loss functions, optimizers, and metrics. Saved the trained models and their training history for future use.; Visualization and Interaction: Implemented methods to plot the training loss and accuracy curves during model training. Created interactive widgets for displaying prediction results and confusion matrices. Linked the selection of prediction options in combo boxes to trigger the corresponding prediction and visualization functions.; Throughout the process, various libraries and frameworks such as scikit-learn, TensorFlow, and Keras are used to perform the tasks efficiently. The overall goal was to train models, evaluate their performance, visualize the results, and provide an interactive experience for the user to explore different prediction options.
THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
The Applied Data Science Workshop on Prostate Cancer Classification and Recognition using Machine Learning and Deep Learning with Python GUI involved several steps and components. The project aimed to analyze prostate cancer data, explore the features, develop machine learning models, and create a graphical user interface (GUI) using PyQt5. The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable. The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter plots, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer. Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning. The performance of each machine learning model was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provided insights into the effectiveness of the models in accurately classifying prostate cancer cases. Model comparison and selection were based on their performance and the specific requirements of the project. In addition to the machine learning models, a deep learning model based on an Artificial Neural Network (ANN) was implemented. The ANN architecture consisted of multiple layers, including input, hidden, and output layers. The ANN model was trained using the dataset, and its performance was evaluated using accuracy and loss metrics. To provide a user-friendly interface for the project, a GUI was designed using PyQt, a Python library for creating desktop applications. The GUI allowed users to interact with the machine learning models and perform tasks such as selecting the prediction method, loading data, training models, and displaying results. The GUI included various graphical components such as buttons, combo boxes, input fields, and plot windows. These components were designed to facilitate data loading, model training, and result visualization. Users could choose the prediction method, view accuracy scores, classification reports, and confusion matrices, and explore the predicted values compared to the actual values. The GUI also incorporated interactive features such as real-time updates of prediction results based on user selections and dynamic plot generation for visualizing model performance. Users could switch between different prediction methods, observe changes in accuracy, and examine the history of training loss and accuracy through plotted graphs. Data preprocessing techniques, such as standardization and normalization, were applied to ensure the consistency and reliability of the machine learning and deep learning models. The dataset was divided into training and testing sets to assess model performance on unseen data and detect overfitting or underfitting. Model persistence was implemented to save the trained machine learning and deep learning models to disk, allowing for easy retrieval and future use. The saved models could be loaded and utilized within the GUI for prediction tasks without the need for retraining. Overall, the Applied Data Science Workshop on Prostate Cancer Classification and Recognition provided a comprehensive framework for analyzing prostate cancer data, developing machine learning and deep learning models, and creating an interactive GUI. The project aimed to assist in the accurate classification and recognition of prostate cancer cases, facilitating informed decision-making and potentially contributing to improved patient outcomes.
Data Science For Programmer: A Project-Based Approach With Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.
COVID-19: Analysis, Classification, and Detection Using Scikit-Learn, Keras, and TensorFlow with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this comprehensive project, "COVID-19: Analysis, Classification, and Detection Using Scikit-Learn, Keras, and TensorFlow with Python GUI," the primary objective is to leverage various machine learning and deep learning techniques to analyze and classify COVID-19 cases based on numerical data and medical image data. The project begins by exploring the dataset, gaining insights into its structure and content. This initial data exploration aids in understanding the distribution of categorized features, providing valuable context for subsequent analysis. With insights gained from data exploration, the project delves into predictive modeling using machine learning. It employs Scikit-Learn to build and fine-tune predictive models, harnessing grid search for hyperparameter optimization. This meticulous process ensures that the machine learning models, such as Naïve Bayes, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, AdaBoost, and Logistic Regression, are optimized to accurately predict the risk of COVID-19 based on the input features. Transitioning to the realm of deep learning, the project employs Convolutional Neural Networks (CNNs) to perform intricate image classification tasks. Leveraging Keras and TensorFlow, the CNN architecture is meticulously crafted, comprising convolutional and pooling layers, dropout regularization, and dense layers. The project also extends its deep learning capabilities by utilizing the VGG16 pre-trained model, harnessing its powerful feature extraction capabilities for COVID-19 image classification. To gauge the effectiveness of the trained models, an array of performance metrics is utilized. In this project, a range of metrics are used to evaluate the performance of machine learning and deep learning models employed for COVID-19 classification. These metrics include Accuracy, which measures the overall correctness of predictions; Precision, emphasizing the accuracy of positive predictions; Recall (Sensitivity), assessing the model's ability to identify positive instances; and F1-Score, a balanced measure of accuracy. The Mean Squared Error (MSE) quantifies the magnitude of errors in regression tasks, while the Confusion Matrix summarizes classification results by showing counts of true positives, true negatives, false positives, and false negatives. These metrics together provide a comprehensive understanding of model performance. They help gauge the model's accuracy, the balance between precision and recall, and its proficiency in classifying both positive and negative instances. In the medical context of COVID-19 classification, these metrics play a vital role in evaluating the models' reliability and effectiveness in real-world applications. The project further enriches its analytical capabilities by developing an interactive Python GUI. This graphical user interface streamlines the user experience, facilitating data input, model training, and prediction. Users are empowered to input medical images for classification, leveraging the trained machine learning and deep learning models to assess COVID-19 risk. The culmination of the project lies in the accurate prediction of COVID-19 risk through a combined approach of machine learning and deep learning techniques. The Python GUI using PyQt5 provides a user-friendly platform for clinicians and researchers to interact with the models, fostering informed decision-making based on reliable and data-driven predictions. In conclusion, this project represents a comprehensive endeavor to harness the power of machine learning and deep learning for the vital task of COVID-19 classification. Through rigorous data exploration, model training, and performance evaluation, the project yields a robust framework for risk prediction, contributing to the broader efforts to combat the ongoing pandemic.
Data Science and Deep Learning Workshop For Scientists and Engineers by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
WORKSHOP 1: In this workshop, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. WORKSHOP 2: In this workshop, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. WORKSHOP 3: In this workshop, you will implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). WORKSHOP 4: In this workshop, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). WORKSHOP 5: In this workshop, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). WORKSHOP 6: In this worksshop, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle (https://www.kaggle.com/fedesoriano/traffic-prediction-dataset/download). This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle (https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset/download). WORKSHOP 7: In this workshop, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle (https://www.kaggle.com/ishandutta/early-stage-diabetes-risk-prediction-dataset/download). This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle (https://www.kaggle.com/merishnasuwal/breast-cancer-prediction-dataset/download). Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. WORKSHOP 8: In this workshop, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy. WORKSHOP 9: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform COVID-19 Epitope Prediction using COVID-19/SARS B-cell Epitope Prediction dataset provided in Kaggle. All of three datasets consists of information of protein and peptide: parent_protein_id : parent protein ID; protein_seq : parent protein sequence; start_position : start position of peptide; end_position : end position of peptide; peptide_seq : peptide sequence; chou_fasman : peptide feature; emini : peptide feature, relative surface accessibility; kolaskar_tongaonkar : peptide feature, antigenicity; parker : peptide feature, hydrophobicity; isoelectric_point : protein feature; aromacity: protein feature; hydrophobicity : protein feature; stability : protein feature; and target : antibody valence (target value). The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, XGB classifier, and MLP classifier. Then, you will learn how to use sequential CNN and VGG16 models to detect and predict Covid-19 X-RAY using COVID-19 Xray Dataset (Train & Test Sets) provided in Kaggle. The folder itself consists of two subfolders: test and train. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy. WORKSHOP 10: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform analyzing and predicting stroke using dataset provided in Kaggle. The dataset consists of attribute information: id: unique identifier; gender: "Male", "Female" or "Other"; age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension; heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease; ever_married: "No" or "Yes"; work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"; Residence_type: "Rural" or "Urban"; avg_glucose_level: average glucose level in blood; bmi: body mass index; smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"; and stroke: 1 if the patient had a stroke or 0 if not. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 11: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform classifying and predicting Hepatitis C using dataset provided by UCI Machine Learning Repository. All attributes in dataset except Category and Sex are numerical. Attributes 1 to 4 refer to the data of the patient: 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. The target attribute for classification is Category (2): blood donors vs. Hepatitis C patients (including its progress ('just' Hepatitis C, Fibrosis, Cirrhosis). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and ANN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy.
DATA SCIENCE WORKSHOP: Cervical Cancer Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
This book titled " Data Science Workshop: Cervical Cancer Classification and Prediction using Machine Learning and Deep Learning with Python GUI" embarks on an insightful journey starting with an in-depth exploration of the dataset. This dataset encompasses various features that shed light on patients' medical histories and attributes. Utilizing the capabilities of pandas, the dataset is loaded, and essential details like data dimensions, column names, and data types are scrutinized. The presence of missing data is addressed by employing suitable strategies such as mean-based imputation for numerical features and categorical encoding for non-numeric ones. Subsequently, the project delves into an illuminating visualization of categorized feature distributions. Through the ingenious use of pie charts, bar plots, and heatmaps, the project unveils the distribution patterns of key attributes such as 'Hormonal Contraceptives,' 'Smokes,' 'IUD,' and others. These visualizations illuminate potential relationships between these features and the target variable 'Biopsy,' which signifies the presence or absence of cervical cancer. Such exploratory analyses serve as a vital foundation for identifying influential trends within the dataset. Transitioning into the core phase of predictive modeling, the workshop orchestrates a meticulous ensemble of machine learning models to forecast cervical cancer outcomes. The repertoire includes Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, Naïve Bayes, and the power of ensemble methods like AdaBoost and XGBoost. The models undergo rigorous hyperparameter tuning facilitated by Grid Search and Random Search to optimize predictive accuracy and precision. As the workshop progresses, the spotlight shifts to the realm of deep learning, introducing advanced neural network architectures. An Artificial Neural Network (ANN) featuring multiple hidden layers is trained using the backpropagation algorithm. Long Short-Term Memory (LSTM) networks are harnessed to capture intricate temporal relationships within the data. The arsenal extends to include Self Organizing Maps (SOMs), Restricted Boltzmann Machines (RBMs), and Autoencoders, showcasing the efficacy of unsupervised feature learning and dimensionality reduction techniques. 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. Culminating on a high note, the workshop concludes with the creation of a Python GUI utilizing PyQt. This intuitive graphical user interface empowers users to input pertinent medical data and receive instant predictions regarding their cervical cancer risk. Seamlessly integrating the most proficient classification model, this user-friendly interface bridges the gap between sophisticated data science techniques and practical healthcare applications. In this comprehensive workshop, participants navigate through the intricate landscape of data exploration, preprocessing, feature visualization, predictive modeling encompassing both traditional and deep learning paradigms, robust performance evaluation, and culminating in the development of an accessible and informative GUI. The project aspires to provide healthcare professionals and individuals with a potent tool for early cervical cancer detection and prognosis.
Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM by Mubashir Tariq,Aized Amin Sufi,Habib ur Rehman,Syed Kamran Ajmal,Danish Riaz,Muhammad Amjad,Bilal Ahmad,Muhammad Haris Raza Pdf
In the domain of Medical Image Analysis (MIA), it is difficult to perform brain tumor classification. With the help of machine learning technology and algorithms, brain tumor can be easily diagnosed by the radiologists without practicing any surgical approach. In the previous few years, remarkable progress has been observed by deep learning techniques in the domain of MIA. Although, the classification of brain tumor through Magnetic Resonance Imaging (MRI) has seen multiple problems: 1) the structure of brain and complexity of brain tissues; 2) deriving the classification of brain tumor due to brain’s nature of high-density. To study the classification of brain tumor; inculcating the normal and abnormal MRI, this study has designed a blended method by using Neutrosophic Super Resolution (NSR) with Fuzzy-C-Means (FCM) and Convolutional Neural Network (CNN).Initially, non-local mean filtered MRI provided Neutrosophic Super Resolution (NSR) image, however, for enhancement of clustering and simulation of the brain tumor along with the reduction of time consumption, efficiency and accuracy without any technical hindrance Support vector Machine (SVM) guided FCM was applied. Consequently, the recommended method resulted in an excellent performance with 98.12%, 98.2% of average success about sensitivity and 1.8% of error rate brain tumor image.
DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan Pdf
In this data science workshop focused on Parkinson's disease classification and prediction, we begin by exploring the dataset containing features relevant to the disease. We perform data exploration to understand the structure of the dataset, check for missing values, and gain insights into the distribution of features. Visualizations are used to analyze the distribution of features and their relationship with the target variable, which is whether an individual has Parkinson's disease or not. After data exploration, we preprocess the dataset to prepare it for machine learning models. This involves handling missing values, scaling numerical features, and encoding categorical variables if necessary. We ensure that the dataset is split into training and testing sets to evaluate model performance effectively. With the preprocessed dataset, we move on to the classification task. Using various machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), we train multiple models on the training data. To optimize the hyperparameters of these models, we utilize Grid Search, a technique to exhaustively search for the best combination of hyperparameters. For each machine learning model, we evaluate their performance on the test set using various metrics such as accuracy, precision, recall, and F1-score. These metrics help us understand the model's ability to correctly classify individuals with and without Parkinson's disease. Next, we delve into building an Artificial Neural Network (ANN) for Parkinson's disease prediction. The ANN architecture is designed with input, hidden, and output layers. We utilize the TensorFlow library to construct the neural network with appropriate activation functions, dropout layers, and optimizers. The ANN is trained on the preprocessed data for a fixed number of epochs, and we monitor its training and validation loss and accuracy to ensure proper training. After training the ANN, we evaluate its performance using the same metrics as the machine learning models, comparing its accuracy, precision, recall, and F1-score against the previous models. This comparison helps us understand the benefits and limitations of using deep learning for Parkinson's disease prediction. To provide a user-friendly interface for the classification and prediction process, we design a Python GUI using PyQt. The GUI allows users to load their own dataset, choose data preprocessing options, select machine learning classifiers, train models, and predict using the ANN. The GUI provides visualizations of the data distribution, model performance, and prediction results for better understanding and decision-making. In the GUI, users have the option to choose different data preprocessing techniques, such as raw data, normalization, and standardization, to observe how these techniques impact model performance. The choice of classifiers is also available, allowing users to compare different models and select the one that suits their needs best. Throughout the workshop, we emphasize the importance of proper evaluation metrics and the significance of choosing the right model for Parkinson's disease classification and prediction. We highlight the strengths and weaknesses of each model, enabling users to make informed decisions based on their specific requirements and data characteristics. Overall, this data science workshop provides participants with a comprehensive understanding of Parkinson's disease classification and prediction using machine learning and deep learning techniques. Participants gain hands-on experience in data preprocessing, model training, hyperparameter tuning, and designing a user-friendly GUI for efficient and effective data analysis and prediction.
STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.
OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In the context of sentiment analysis and opinion mining, this project began with dataset exploration. The dataset, comprising user reviews or social media posts, was examined to understand the sentiment labels' distribution. This analysis provided insights into the prevalence of positive or negative opinions, laying the foundation for sentiment classification. To tackle sentiment classification, we employed a range of machine learning algorithms, including Support Vector, Logistic Regression, K-Nearest Neighbours Classiier, Decision Tree, Random Forest Classifier, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Adaboost Classifiers. These algorithms were combined with different vectorization techniques such as Hashing Vectorizer, Count Vectorizer, and TF-IDF Vectorizer. By converting text data into numerical representations, these models were trained and evaluated to identify the most effective combination for sentiment classification. In addition to traditional machine learning algorithms, we explored the power of recurrent neural networks (RNNs) and their variant, Long Short-Term Memory (LSTM). LSTM is particularly adept at capturing contextual dependencies and handling sequential data. The text data was tokenized and padded to ensure consistent input length, allowing the LSTM model to learn from the sequential nature of the text. Performance metrics, including accuracy, were used to evaluate the model's ability to classify sentiments accurately. Furthermore, we delved into Convolutional Neural Networks (CNNs), another deep learning model known for its ability to extract meaningful features. The text data was preprocessed and transformed into numerical representations suitable for CNN input. The architecture of the CNN model, consisting of embedding, convolutional, pooling, and dense layers, facilitated the extraction of relevant features and the classification of sentiments. Analyzing the results of our machine learning models, we gained insights into their effectiveness in sentiment classification. We observed the accuracy and performance of various algorithms and vectorization techniques, enabling us to identify the models that achieved the highest accuracy and overall performance. LSTM and CNN, being more advanced models, aimed to capture complex patterns and dependencies in the text data, potentially resulting in improved sentiment classification. Monitoring the training history and metrics of the LSTM and CNN models provided valuable insights. We examined the learning progress, convergence behavior, and generalization capabilities of the models. Through the evaluation of performance metrics and convergence trends, we gained an understanding of the models' ability to learn from the data and make accurate predictions. Confusion matrices played a crucial role in assessing the models' predictions. They provided a detailed analysis of the models' classification performance, highlighting the distribution of correct and incorrect classifications for each sentiment category. This analysis allowed us to identify potential areas of improvement and fine-tune the models accordingly. In addition to confusion matrices, visualizations comparing the true values with the predicted values were employed to evaluate the models' performance. These visualizations provided a comprehensive overview of the models' classification accuracy and potential areas for improvement. They allowed us to assess the alignment between the models' predictions and the actual sentiment labels, enabling a deeper understanding of the models' strengths and weaknesses. Overall, the exploration of machine learning, LSTM, and CNN models for sentiment analysis and opinion mining aimed to develop effective tools for understanding public opinions. The results obtained from this project showcased the models' performance, convergence behavior, and their ability to accurately classify sentiments. These insights can be leveraged by businesses and organizations to gain a deeper understanding of the sentiments expressed towards their products or services, enabling them to make informed decisions and adapt their strategies accordingly.
THE APPLIED DATA SCIENCE WORKSHOP: Urinary biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI by Vivian Siahaan Pdf
The Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive journey, commencing with an in-depth exploration of the dataset. During this initial phase, the structure and size of the dataset are thoroughly examined, and the various features it contains are meticulously studied. The principal objective is to understand the relationship between these features and the target variable, which, in this case, is the diagnosis of pancreatic cancer. The distribution of each feature is analyzed, and potential patterns, trends, or outliers that could significantly impact the model's performance are identified. To ensure the data is in optimal condition for model training, preprocessing steps are undertaken. This involves handling missing values through imputation techniques, such as mean, median, or interpolation, depending on the nature of the data. Additionally, feature engineering is performed to derive new features or transform existing ones, with the aim of enhancing the model's predictive power. In preparation for model building, the dataset is split into training and testing sets. This division is crucial to assess the models' generalization performance on unseen data accurately. To maintain a balanced representation of classes in both sets, stratified sampling is employed, mitigating potential biases in the model evaluation process. The workshop explores an array of machine learning classifiers suitable for pancreatic cancer classification, such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, Naïve Bayes, and Multi-Layer Perceptron (MLP). For each classifier, three different preprocessing techniques are applied to investigate their impact on model performance: raw (unprocessed data), normalization (scaling data to a similar range), and standardization (scaling data to have zero mean and unit variance). To optimize the classifiers' hyperparameters and boost their predictive capabilities, GridSearchCV, a technique for hyperparameter tuning, is employed. GridSearchCV conducts an exhaustive search over a specified hyperparameter grid, evaluating different combinations to identify the optimal settings for each model and preprocessing technique. During the model evaluation phase, multiple performance metrics are utilized to gauge the efficacy of the classifiers. Commonly used metrics include accuracy, recall, precision, and F1-score. By comprehensively assessing these metrics, the strengths and weaknesses of each model are revealed, enabling a deeper understanding of their performance across different classes of pancreatic cancer. Classification reports are generated to present a detailed breakdown of the models' performance, including precision, recall, F1-score, and support for each class. These reports serve as valuable tools for interpreting model outputs and identifying areas for potential improvement. The workshop highlights the significance of graphical user interfaces (GUIs) in facilitating user interactions with machine learning models. By integrating PyQt, a powerful GUI development library for Python, participants create a user-friendly interface that enables users to interact with the models effortlessly. The GUI provides options to select different preprocessing techniques, visualize model outputs such as confusion matrices and decision boundaries, and gain insights into the models' classification capabilities. One of the primary advantages of the graphical user interface is its ability to offer users a seamless and intuitive experience in predicting and classifying pancreatic cancer based on urinary biomarkers. The GUI empowers users to make informed decisions by allowing them to compare the performance of different classifiers under various preprocessing techniques. Throughout the workshop, a strong emphasis is placed on the significance of proper data preprocessing, hyperparameter tuning, and robust model evaluation. These crucial steps contribute to building accurate and reliable machine learning models for pancreatic cancer prediction. By the culmination of the workshop, participants have gained valuable hands-on experience in data exploration, machine learning model building, hyperparameter tuning, and GUI development, all geared towards addressing the specific challenge of pancreatic cancer classification and prediction. In conclusion, the Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive and transformative journey, bringing together data exploration, preprocessing, machine learning model selection, hyperparameter tuning, model evaluation, and GUI development. The project's focus on pancreatic cancer prediction using urinary biomarkers aligns with the pressing need for early detection and treatment of this deadly disease. As participants delve into the intricacies of machine learning and medical research, they contribute to the broader scientific community's ongoing efforts to combat cancer and improve patient outcomes. Through the integration of data science methodologies and powerful visualization tools, the workshop exemplifies the potential of machine learning in revolutionizing medical diagnostics and healthcare practices.
DATA SCIENCE WORKSHOP: Alzheimer’s Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan Pdf
In the "Data Science Workshop: Alzheimer's Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI," the project aimed to address the critical task of Alzheimer's disease prediction. The journey began with a comprehensive data exploration phase, involving the analysis of a dataset containing various features related to brain scans and demographics of patients. This initial step was crucial in understanding the data's characteristics, identifying missing values, and gaining insights into potential patterns that could aid in diagnosis. Upon understanding the dataset, the categorical features' distributions were meticulously examined. The project expertly employed pie charts, bar plots, and stacked bar plots to visualize the distribution of categorical variables like "Group," "M/F," "MMSE," "CDR," and "age_group." These visualizations facilitated a clear understanding of the demographic and clinical characteristics of the patients, highlighting key factors contributing to Alzheimer's disease. The analysis revealed significant patterns, such as the prevalence of Alzheimer's in different age groups, gender-based distribution, and cognitive performance variations. Moving ahead, the project ventured into the realm of predictive modeling. Employing machine learning techniques, the team embarked on a journey to develop models capable of predicting Alzheimer's disease with high accuracy. The focus was on employing various machine learning algorithms, including K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting, Light Gradient Boosting, Multi-Layer Perceptron, and Extreme Gradient Boosting. Grid search was applied to tune hyperparameters, optimizing the models' performance. The evaluation process was meticulous, utilizing a range of metrics such as accuracy, precision, recall, F1-score, and confusion matrices. This intricate analysis ensured a comprehensive assessment of each model's ability to predict Alzheimer's cases accurately. The project further delved into deep learning methodologies to enhance predictive capabilities. An arsenal of deep learning architectures, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN), were employed. These models leveraged the intricate relationships present in the data to make refined predictions. The evaluation extended to ROC curves and AUC scores, providing insights into the models' ability to differentiate between true positive and false positive rates. The project also showcased an innovative Python GUI built using PyQt. This graphical interface provided a user-friendly platform to input data and visualize the predictions. The GUI's interactive nature allowed users to explore model outcomes and predictions while seamlessly navigating through different input options. In conclusion, the "Data Science Workshop: Alzheimer's Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI" was a comprehensive endeavor that involved meticulous data exploration, distribution analysis of categorical features, and extensive model development and evaluation. It skillfully navigated through machine learning and deep learning techniques, deploying a variety of algorithms to predict Alzheimer's disease. The focus on diverse metrics ensured a holistic assessment of the models' performance, while the innovative GUI offered an intuitive platform to engage with predictions interactively. This project stands as a testament to the power of data science in tackling complex healthcare challenges.
The Practical Guides on Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display image histogram. It is a graphical representation that displays the distribution of pixel intensities in an image. It provides information about the frequency of occurrence of each intensity level in the image. The histogram allows us to understand the overall brightness or contrast of the image and can reveal important characteristics such as dynamic range, exposure, and the presence of certain image features. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. The MNIST dataset is a widely used dataset in machine learning and computer vision, particularly for image classification tasks. It consists of a collection of handwritten digits from zero to nine, where each digit is represented as a 28x28 grayscale image. The dataset was created by collecting handwriting samples from various individuals and then preprocessing them to standardize the format. Each image in the dataset represents a single digit and is labeled with the corresponding digit it represents. The labels range from 0 to 9, indicating the true value of the handwritten digit. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset. Following are the steps taken in this chapter: Dataset Exploration: Explore the Brain Image MRI dataset from Kaggle. Describe the structure of the dataset, the different classes (tumor vs. non-tumor), and any preprocessing steps required; Data Preprocessing: Preprocess the dataset to prepare it for model training. This may include tasks such as resizing images, normalizing pixel values, splitting data into training and testing sets, and creating labels; Model Building: Use TensorFlow and Keras to build a deep learning model for brain tumor detection. Choose an appropriate architecture, such as a convolutional neural network (CNN), and configure the model layers; Model Training: Train the brain tumor detection model using the preprocessed dataset. Specify the loss function, optimizer, and evaluation metrics. Monitor the training process and visualize the training/validation accuracy and loss over epochs; Model Evaluation: Evaluate the trained model on the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Prediction and Visualization: Use the trained model to make predictions on new MRI images. Visualize the predicted results alongside the ground truth labels to demonstrate the effectiveness of the model. Finally, you will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle using MobileNetV2 and CNN models. Following are the steps taken in this chapter: Data Exploration: Load the dataset using Pandas, perform exploratory data analysis (EDA) to gain insights into the data, and visualize the distribution of gender classes; Data Preprocessing: Preprocess the dataset by performing necessary transformations, such as resizing images, converting labels to numerical format, and splitting the data into training, validation, and test sets; Model Building: Use TensorFlow and Keras to build a gender classification model. Define the architecture of the model, compile it with appropriate loss and optimization functions, and summarize the model's structure; Model Training: Train the model on the training set, monitor its performance on the validation set, and tune hyperparameters if necessary. Visualize the training history to analyze the model's learning progress; Model Evaluation: Evaluate the trained model's performance on the test set using various metrics such as accuracy, precision, recall, and F1 score. Generate a classification report and a confusion matrix to assess the model's performance in detail; Prediction and Visualization: Use the trained model to make gender predictions on new, unseen data. Visualize a few sample predictions along with the corresponding images. Finally, you will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset using CNN model. The FER2013 dataset contains facial images categorized into seven different emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. To perform facial expression recognition using this dataset, you would typically follow these steps; Data Preprocessing: Load and preprocess the dataset. This may involve resizing the images, converting them to grayscale, and normalizing the pixel values; Data Split: Split the dataset into training, validation, and testing sets. The training set is used to train the model, the validation set is used to tune hyperparameters and evaluate the model's performance during training, and the testing set is used to assess the final model's accuracy; Model Building: Build a deep learning model using TensorFlow and Keras. This typically involves defining the architecture of the model, selecting appropriate layers (such as convolutional layers, pooling layers, and fully connected layers), and specifying the activation functions and loss functions; Model Training: Train the model using the training set. This involves feeding the training images through the model, calculating the loss, and updating the model's parameters using optimization techniques like backpropagation and gradient descent; Model Evaluation: Evaluate the trained model's performance using the validation set. This can include calculating metrics such as accuracy, precision, recall, and F1 score to assess how well the model is performing; Model Testing: Assess the model's accuracy and performance on the testing set, which contains unseen data. This step helps determine how well the model generalizes to new, unseen facial expressions; Prediction: Use the trained model to make predictions on new images or live video streams. This involves detecting faces in the images using OpenCV, extracting facial features, and feeding the processed images into the model for prediction. Then, you will also build a GUI application for this purpose.
DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using machine learning and deep learning techniques, all implemented with the power of Python and a user-friendly GUI built with PyQt. We will start by conducting data exploration on a comprehensive dataset containing relevant features and thyroid disease labels. Through analysis and pattern recognition, we will gain insights into the underlying factors contributing to thyroid disease. Next, we will delve into the machine learning phase, where we will implement popular algorithms including Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, and Multi-Layer Perceptron. These models will be trained using different preprocessing techniques, including raw data, normalization, and standardization, to evaluate their performance and accuracy. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. Moving beyond traditional machine learning, we will build an artificial neural network (ANN) using TensorFlow. This ANN will capture complex relationships within the data and provide accurate predictions of thyroid disease. To ensure the effectiveness of our ANN, we will train it using a curated dataset split into training and testing sets. This will allow us to evaluate the model's performance and its ability to generalize predictions. To provide an interactive and user-friendly experience, we will develop a Graphical User Interface (GUI) using PyQt. The GUI will allow users to input data, select prediction methods (machine learning or deep learning), and visualize the results. Through the GUI, users can explore different prediction methods, compare performance, and gain insights into thyroid disease classification. Visualizations of training and validation loss, accuracy, and confusion matrices will enhance understanding and model evaluation. Line plots comparing true values and predicted values will further aid interpretation and insights into classification outcomes. Throughout the project, we will emphasize the importance of preprocessing techniques, feature selection, and model evaluation in building reliable and effective thyroid disease classification and prediction models. By the end of the project, readers will have gained practical knowledge in data exploration, machine learning, deep learning, and GUI development. They will be equipped to apply these techniques to other domains and real-world challenges. The project’s comprehensive approach, from data exploration to model development and GUI implementation, ensures a holistic understanding of thyroid disease classification and prediction. It empowers readers to explore applications of data science in healthcare and beyond. The combination of machine learning and deep learning techniques, coupled with the intuitive GUI, offers a powerful framework for thyroid disease classification and prediction. This project serves as a stepping stone for readers to contribute to the field of medical data science. Data-driven approaches in healthcare have the potential to unlock valuable insights and improve outcomes. The focus on thyroid disease classification and prediction in this session showcases the transformative impact of data science in the medical field. Together, let us embark on this journey to advance our understanding of thyroid disease and make a difference in the lives of individuals affected by this condition. Welcome to the Data Science Crash Course on Thyroid Disease Classification and Prediction!
HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
The purpose of this project is to develop a comprehensive Hate Speech Detection and Sentiment Analysis system using both Machine Learning and Deep Learning techniques. The project aims to create a robust and accurate system that can automatically identify hate speech in text data and perform sentiment analysis to determine the emotions and opinions expressed in the text. The project is designed to address the growing concern over the spread of hate speech and offensive content online. By implementing an automated detection system, it can help social media platforms, content moderators, and online communities to proactively identify and remove harmful content, fostering a safer and more inclusive online environment. Additionally, sentiment analysis plays a crucial role in understanding public opinions, customer feedback, and social media trends. By accurately predicting sentiment, businesses can make data-driven decisions, improve customer satisfaction, and gain valuable insights into consumer preferences. This project focuses on Hate Speech Detection and Sentiment Analysis using both Machine Learning and Deep Learning techniques. It begins with exploring the dataset, analyzing feature distributions, and predicting sentiment using Machine Learning models like Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and AdaBoost, while optimizing their performance through Grid Search for hyperparameter tuning. Subsequently, Deep Learning LSTM and 1D CNN models are implemented for sentiment analysis to capture long-term dependencies and local patterns in the text data. The project starts with exploring the dataset, understanding its structure, and analyzing the distribution of classes for hate speech and sentiment labels. This initial step allows us to gain insights into the dataset and potential challenges. After exploring the data, the distribution of text features, such as word frequency and sentiment scores, is analyzed to identify any patterns or biases that could impact the model's performance. The dataset is then divided into training, validation, and testing sets to evaluate the models' generalization capabilities. Early stopping techniques are utilized during training to prevent overfitting and enhance model generalization. Performance evaluation involves calculating metrics like accuracy, precision, recall, and F1-score to gauge the models' effectiveness. Confusion matrices and visualizations provide further insights into model predictions and potential areas for improvement. A graphical user interface (GUI) is developed using PyQt to facilitate user interaction with the Hate Speech Detection and Sentiment Analysis system. Before training the Deep Learning models, the text data is tokenized and padded for uniform input sequences. The dataset is split into training and validation sets for model evaluation, and early stopping is used to prevent overfitting during training. The final system combines predictions from both Machine Learning and Deep Learning models to provide robust sentiment analysis results. The PyQt GUI allows users to input text and receive real-time sentiment analysis predictions. The LSTM and 1D CNN models, along with their optimized hyperparameters, are saved and deployed for future sentiment analysis tasks. Users can interact with the GUI, analyze sentiment in different texts, and provide feedback for continuous improvement of the Hate Speech Detection and Sentiment Analysis system.
