000  03594nam a2200325 i 4500  

005  20230526220505.0  
008  210823s2019 enka  00 0 eng d  
020 
_a9781838551025 _qpaperback 

040 
_cEGCaTKH _beng _dEGCaTKH _erda 

050 
_aQA76.73.P98 _b.K567 2019 

082  0  0 
_a006.33 KL.D 2019 _223 
100  1 
_aKlosterman, Stephen, _eauthor. 

245  1  0 
_aData science projects with Python : _ba case study approach to successful data science projects using Python, pandas, and scikcitlearn / _cStephen Klosterman. 
250  _aFirst edition.  
264  1 
_aBirmingham, England : _bPackt Publishing, _c2019. 

300 
_aiv, 353 pages, 5 unnumbered pages : _billustrations ; _c24 cm. 

336 
_2rdacontent _atext _btxt 

337 
_2rdamedia _aunmediated _bn 

338 
_2rdacarrier _avolume _bnc 

500  _aIncludes Index  
505  0  _aData Exploration and Cleaning  Introduction to ScikitLearn and Model Evaluation  Details of Logistic Regression and Feature Exploration  The BiasVariance Tradeoff  Decision Trees and Random Forests  Imputation of Missing Data, Financial Analysis, and Delivery to Client.  
520  _aGain handson experience with industrystandard 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 industrystandard 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 scikitlearn 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 scikitlearn Use lasso and ridge regression to regularize your models Compare performance between models to find the best outcomes Use kfold crossvalidation 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.  
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_2ddc _cBK 

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_ahuda.mahmoud _bM _d20220215 

998 
_amona.romia _bR _d20220216 

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_c876 _d876 