000			04435nam a22001817a 4500
005			20240723121523.0
008			240723b |||||||| |||| 00| 0 eng d
020			_a9781789537253
082			_a005.133 _bSUR
100			_a Mukhiya, Suresh Kumar
245			_aHands-On Exploratory Data Analysis with Python: _bPerform EDA techniques to understand, summarize, and investigate your data _cUsman Ahmed , Suresh Kumar Mukhiya
260			_aUK _bPackt> _c2020
300			_a336
505			_t1. Section 1: The Fundamentals of EDA Section 1: The Fundamentals of EDA 2. Exploratory Data Analysis Fundamentals Exploratory Data Analysis Fundamentals Understanding data science The significance of EDA Making sense of data Comparing EDA with classical and Bayesian analysis Software tools available for EDA Getting started with EDA Summary Further reading 3. Visual Aids for EDA Visual Aids for EDA Technical requirements Line chart Bar charts Scatter plot Area plot and stacked plot Pie chart Table chart Polar chart Histogram Lollipop chart Choosing the best chart Other libraries to explore Summary Further reading 4. EDA with Personal Email EDA with Personal Email Technical requirements Loading the dataset Data transformation Data analysis Summary Further reading 5. Data Transformation Data Transformation Technical requirements Background Merging database-style dataframes Transformation techniques Benefits of data transformation Summary Further reading 6. Section 2: Descriptive Statistics Section 2: Descriptive Statistics 7. Descriptive Statistics Descriptive Statistics Technical requirements Understanding statistics Measures of central tendency Measures of dispersion Summary Further reading 8. Grouping Datasets Grouping Datasets Technical requirements Understanding groupby() Groupby mechanics Data aggregation Pivot tables and cross-tabulations Summary Further reading 9. Correlation Correlation Technical requirements Introducing correlation Types of analysis Discussing multivariate analysis using the Titanic dataset Outlining Simpson's paradox Correlation does not imply causation Summary Further reading 10. Time Series Analysis Time Series Analysis Technical requirements Understanding the time series dataset TSA with Open Power System Data Summary Further reading 11. Section 3: Model Development and Evaluation Section 3: Model Development and Evaluation 12. Hypothesis Testing and Regression Hypothesis Testing and Regression Technical requirements Hypothesis testing p-hacking Understanding regression Model development and evaluation Summary Further reading 13. Model Development and Evaluation Model Development and Evaluation Technical requirements Types of machine learning Understanding supervised learning Understanding unsupervised learning Understanding reinforcement learning Unified machine learning workflow Summary Further reading 14. EDA on Wine Quality Data Analysis EDA on Wine Quality Data Analysis Technical requirements Disclosing the wine quality dataset Analyzing red wine Analyzing white wine Model development and evaluation Summary Further reading
520			_aExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
700			_aUsman Ahmed
942			_2ddc _cBK
999			_c2342 _d2342