Data Preprocessing in Data Science Best Practices and Techniques

Feb 05, 2025

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This PowerPoint presentation provides an in-depth overview of data preprocessing, a crucial step in the data science workflow. It covers the importance of cleaning, transforming, and preparing raw data for analysis to improve model accuracy and performance.<br><br>The presentation highlights key techniques such as handling missing values, outlier detection, feature scaling, encoding categorical data, and dimensionality reduction. Additionally, it explores best practices to ensure data quality, consistency, and efficiency in machine learning applications.

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Data Preprocessing in Data Science Best Practices and Techniques

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Data Preprocessing

Data Preprocessing. Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009. Knowledge Discovery (KDD) Process. Knowledge. Pattern Evaluation. Data mining—core of knowledge discovery process. Data Mining. Task-relevant Data. Selection. Data Warehouse. Data Cleaning.

929 views • 29 slides

Data Preprocessing

Data Preprocessing. CSC 576: Data Science. Today. Data Preprocessing Handling Missing Values Handling Outliers Covariance and Correlation Normalization Binning, Discretization Sampling Aggregation. Data Preprocessing. Data Exploration phase results in finding data quality issues

954 views • 44 slides

Data preprocessing

Data preprocessing. Dr. Yan Liu Department of Biomedical, Industrial and Human Factors Engineering Wright State University. Why Data Preprocessing. “Dirty” Real-World Data Noisy: contain errors or outliers e.g. salary = “-10”

828 views • 42 slides

Data Preprocessing

Data Preprocessing . Lecturer: Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Outline. Data Cleansing Data Transformation Data Description Feature Selection Feature Extraction. Where are data from?. Why Data Preprocessing?. Real data are notoriously dirty!

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Data Preprocessing

Data Preprocessing. Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Multi-Dimensional Measure of Data Quality. A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness

1.33k views • 48 slides

Data Preprocessing

Data Preprocessing. Peter Brezany Institu t für Softwarewissenschaften Universit ät Wien Tel. 4277 38825 Sprechstunde: Di, 12.30-13.30. Today‘s real-world databases are highly susceptible to noisy , missing , and inconsistent data .

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Data Mining: Data Preprocessing

Data Mining: Data Preprocessing. Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?.

1.44k views • 76 slides

Data Preprocessing

Data Preprocessing. Lecture 12: Overview of data preprocessing Lecture 13: Descriptive data summarization Lecture 14: Data cleaning Lecture 15: Data integration/transformation and data reduction Lecture 16: Discretization and concept hierarchy generation and summary. Discretization.

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Data Preprocessing

Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

1.29k views • 75 slides

Data Engineering Data preprocessing and transformation

Data Engineering Data preprocessing and transformation. Just apply a learner? No!. Algorithms are biased No free lunch theorem: considering all possible data distributions, no algorithm is better than another Algorithms make assumptions about data

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Data Preprocessing

Data Preprocessing. Data – Things to consider. Type of data: determines which tools to analyze the data Quality of data: Tolerate some levels of imperfection Improve quality of data improves the quality of the results Preprocessing: modify the data to better fit data mining tools:

775 views • 48 slides

Data Preprocessing

Data Preprocessing. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

877 views • 48 slides

Data Preprocessing

Data Preprocessing. By K.K.Ezhilarasan , Asst Prof./CSE, SET,SGI. Outline. Introduction Descriptive Data Summarization Data Cleaning Missing value Noise data Data Integration Redundancy Data Transformation. Knowledge Discovery (KDD) Process. Knowledge. Pattern Evaluation.

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Data Preprocessing

Data Preprocessing. CS 536 – Data Mining These slides are adapted from J. Han and M. Kamber’s book slides (http://www.cs.sfu.ca/~han). Representation of Data. Data can be represented in different ways Different types of values are used for attributes or features

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Data Preprocessing

Data Preprocessing. Relational Databases - Normalization Denormalization Data Preprocessing Missing Data Missing values and the 3VL approach Problems with 3VL approach Special Values. Remember: Relational Databases. Model entities and relationships

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Data Preprocessing

Data Preprocessing. Chapter 2. Chapter Objectives. Realize the importance of data preprocessing for real world data before data mining or construction of data warehouses. Get an overview of some data preprocessing issues and techniques. The course. (4). DS. OLAP. (2). (3). Data

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Data Preprocessing

Data Preprocessing. The need of data preprocessing. Problems with huge real-world database Incomplete data : missing value Noisy Inconsistent  Influence data mining process, especially pattern mined. Techniques. Data cleaning Data integration Data transformation Data reduction

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Data Preprocessing

Data Preprocessing. Dr Lewis RSU Geography UCL. Rationale. when we view an RS image, we see a 'picture’. Rationale. when we view an RS image, we see a 'picture’. BUT need to be aware of the 'image formation process' to:

174 views • 11 slides

Data Preprocessing

187 views • 16 slides

Data PreProcessing

Data PreProcessing. Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation. Why Data Preprocessing?. Data in the real world is dirty

1.17k views • 101 slides

Data Exploration and Preprocessing

Become an expert in data science and machine learning with ExcelR's comprehensive Data Science Course. Learn the skills employers are looking for and embark on a rewarding career.

20 views • 2 slides

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