Data Preprocessing for Classifying Medical Dataset M. S. Padmavathi

Data Preprocessing for Classifying Medical Dataset

Author: M. S. Padmavathi
$35.99 3599
Out Stock
Successful pre-order.Thanks for contacting us!
Book Title
Data Preprocessing for Classifying Medical Dataset
Author
M. S. Padmavathi
ISBN
9783150626191
The book would provide an in-depth guide to the various techniques and methods used in preprocessing and preparing medical datasets for classification tasks. The book would start by discussing the importance of data preprocessing in machine learning and how it affects the overall performance of a classifier.It would then cover various topics such as data cleaning, data transformation, normalization, outlier detection, and imputation, with a focus on their applications in medical datasets. The book would also delve into feature scaling, selection, and encoding categorical variables, providing readers with practical examples and case studies.Additionally, the book would explore the challenges posed by class imbalance and multi-collinearity in medical datasets, and provide techniques for data balancing and data reduction. The book would also provide guidance on feature engineering and its impact on the performance of classifiers.The book would be aimed at data scientists, machine learning engineers, and medical professionals with a background in data analysis and programming who are interested in using machine learning to classify medical datasets. The book would provide a comprehensive and hands-on approach to preprocessing medical datasets for classification tasks, equipping readers with the knowledge and skills necessary to tackle real-world problems.Data mining engine can perform functions like Characterization, Association and Correlation Analysis, Classification, Prediction, Cluster analysis, Sequential patterns, Outlier analysis, and Evolution analysis. Besides the effectiveness of data mining, there are also many challenges faced while performing data mining task. The factors influencing data mining are: Mining Methodology and User Interaction, Performance Issues, Diverse Data Types, Uncertainty Handling, Dealing with Missing Values and Outliers, Efficiency of Algorithms, Incorporating Domain Knowledge, Sizeand Complexity of Data.Binding Type: PaperbackAuthor: M. S. PadmavathiPublisher: Mrs. M.S PadmavathiPublished: 02/01/2023ISBN: 9783150626191Pages: 216Weight: 0.65lbsSize: 9.00h x 6.00w x 0.46d

The book would provide an in-depth guide to the various techniques and methods used in preprocessing and preparing medical datasets for classification tasks. The book would start by discussing the importance of data preprocessing in machine learning and how it affects the overall performance of a classifier.


It would then cover various topics such as data cleaning, data transformation, normalization, outlier detection, and imputation, with a focus on their applications in medical datasets. The book would also delve into feature scaling, selection, and encoding categorical variables, providing readers with practical examples and case studies.


Additionally, the book would explore the challenges posed by class imbalance and multi-collinearity in medical datasets, and provide techniques for data balancing and data reduction. The book would also provide guidance on feature engineering and its impact on the performance of classifiers.


The book would be aimed at data scientists, machine learning engineers, and medical professionals with a background in data analysis and programming who are interested in using machine learning to classify medical datasets. The book would provide a comprehensive and hands-on approach to preprocessing medical datasets for classification tasks, equipping readers with the knowledge and skills necessary to tackle real-world problems.


Data mining engine can perform functions like Characterization, Association and Correlation Analysis, Classification, Prediction, Cluster analysis, Sequential patterns, Outlier analysis, and Evolution analysis. Besides the

effectiveness of data mining, there are also many challenges faced while performing data

mining task. The factors influencing data mining are: Mining Methodology and User

Interaction, Performance Issues, Diverse Data Types, Uncertainty Handling, Dealing with Missing

Values and Outliers, Efficiency of Algorithms, Incorporating Domain Knowledge, Size

and Complexity of Data.




Binding Type: Paperback
Author: M. S. Padmavathi
Publisher: Mrs. M.S Padmavathi
Published: 02/01/2023
ISBN: 9783150626191
Pages: 216
Weight: 0.65lbs
Size: 9.00h x 6.00w x 0.46d

The book would provide an in-depth guide to the various techniques and methods used in preprocessing and preparing medical datasets for classification tasks. The book would start by discussing the importance of data preprocessing in machine learning and how it affects the overall performance of a classifier.


It would then cover various topics such as data cleaning, data transformation, normalization, outlier detection, and imputation, with a focus on their applications in medical datasets. The book would also delve into feature scaling, selection, and encoding categorical variables, providing readers with practical examples and case studies.


Additionally, the book would explore the challenges posed by class imbalance and multi-collinearity in medical datasets, and provide techniques for data balancing and data reduction. The book would also provide guidance on feature engineering and its impact on the performance of classifiers.


The book would be aimed at data scientists, machine learning engineers, and medical professionals with a background in data analysis and programming who are interested in using machine learning to classify medical datasets. The book would provide a comprehensive and hands-on approach to preprocessing medical datasets for classification tasks, equipping readers with the knowledge and skills necessary to tackle real-world problems.


Data mining engine can perform functions like Characterization, Association and Correlation Analysis, Classification, Prediction, Cluster analysis, Sequential patterns, Outlier analysis, and Evolution analysis. Besides the

effectiveness of data mining, there are also many challenges faced while performing data

mining task. The factors influencing data mining are: Mining Methodology and User

Interaction, Performance Issues, Diverse Data Types, Uncertainty Handling, Dealing with Missing

Values and Outliers, Efficiency of Algorithms, Incorporating Domain Knowledge, Size

and Complexity of Data.




Binding Type: Paperback
Author: M. S. Padmavathi
Publisher: Mrs. M.S Padmavathi
Published: 02/01/2023
ISBN: 9783150626191
Pages: 216
Weight: 0.65lbs
Size: 9.00h x 6.00w x 0.46d