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34.1 MB
Info Hash:
6FA2AF3D977E251DF1BE075384FA33F6AE26C531
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Added:
May 31, 2025, 11:36 a.m.
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(Last updated: June 2, 2025, 4:31 a.m.)
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| ['Sundararajan S. Multivariate Analysis and ML Techniques...Using Python 2025.pdf'] | 0 bytes |
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34.1 MB
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2025-05-31
| Uploaded by andryold1 | Size 34.1 MB | Health [ 43 /36 ] | Added 2025-05-31 |
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405.5 MB
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| Uploaded by indexFroggy | Size 405.5 MB | Health [ 3 /2 ] | Added 2024-07-07 |
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2025-09-25
| Uploaded by andryold1 | Size 32.1 MB | Health [ 82 /47 ] | Added 2025-09-25 |
NOTE
SOURCE: Sundararajan S. Multivariate Analysis and ML Techniques...Using Python 2025
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MEDIAINFO
Textbook in PDF format This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) Machine Learning and Deep Learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensiveintroduction to the emerging and powerful Python ecosystem, which is pervasive in Data Science and Machine Learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any Data Science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, Machine Learning, Deep Learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals. Pandas is a powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language. Pandas can access multiple file formats such as CSV (comma-separated values), excel, JSON, HTML, flat files with a fixed format, flat files with delimited values, clipboard, SQL, parquet (flat columns), pickle (to serialize/de-serialize objects for parallel processing), Google big query, ORC, SAS, SPSS, and STATA. Pandas DataFrame is a 2D (two-dimensional) data structure, like a spreadsheet, with rows and columns. The Pandas project aims to become the most powerful and flexible open-source data analysis/manipulation tool available in any language. Pandas is actively supported by a vibrant community worldwide who contribute significantly to making open-source pandas possible. The Pandas’ community experts provide immense support through Stack Overflow
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