000 03594nam a2200325 i 4500
005 20230526220505.0
008 210823s2019 enka|||| |||| 00| 0 eng d
020 _a9781838551025
040 _cEG-CaTKH
050 _aQA76.73.P98
_b.K567 2019
082 0 0 _a006.33 KL.D 2019
100 1 _aKlosterman, Stephen,
245 1 0 _aData science projects with Python :
_ba case study approach to successful data science projects using Python, pandas, and scikcit-learn /
_cStephen Klosterman.
250 _aFirst edition.
264 1 _aBirmingham, England :
_bPackt Publishing,
300 _aiv, 353 pages, 5 unnumbered pages :
_billustrations ;
_c24 cm.
336 _2rdacontent
337 _2rdamedia
338 _2rdacarrier
500 _aIncludes Index
505 0 _aData Exploration and Cleaning -- Introduction to Scikit--Learn and Model Evaluation -- Details of Logistic Regression and Feature Exploration -- The Bias-Variance Trade-off -- Decision Trees and Random Forests -- Imputation of Missing Data, Financial Analysis, and Delivery to Client.
520 _aGain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Tackle data science problems by identifying the problem to be solved Illustrate patterns in data using appropriate visualizations Implement suitable machine learning algorithms to gain insights from data Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You'll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you'll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter notebook running Python Use Matplotlib to create data visualizations Fit machine learning models using scikit-learn Use lasso and ridge regression to regularize your models Compare performance between models to find the best outcomes Use k-fold cross-validation to select model hyperparameters Who this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.
650 0 _aPython (Computer program language).
650 0 _aMachine learning.
650 0 _aData mining.
650 0 _aInformation visualization.
942 _2ddc
998 _ahuda.mahmoud
998 _amona.romia
999 _c876