Practical Data Science with Jupyter Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine… (Prateek Gupta)（Z-Library）

Author： Prateek Gupta

Solve business problems with data-driven techniques and easy-to-follow Python examples Key Features Essential coverage on statistics and data science techniques. Exposure to Jupyter, PyCharm, and use of GitHub. Real use-cases, best practices, and smart techniques on the use of data science for data applications. Description This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. What you will learn Rapid understanding of Python concepts for data science applications. Understand and practice how to run data analysis with data science techniques and algorithms. Learn feature engineering, dealing with different datasets, and most trending machine le

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【One-Line Pitch】 Solve business problems with data-driven techniques and easy-to-follow Python examples Key Features Essential coverag… 【Book Arc】 - **Opening (~0%–12%)**: Exposure to Jupyter, PyCharm, and use of GitHub.; Objective What is time-series forecasting? - **Early (~12%–35%)**: s assigns labels (0 for the first element, 1 for the second element, and so on).; ected to our engine, so no need to use the ENGINE parameter. - **Middle (~35%–65%)**: You can access the data from our GitHub repository.; Line graphs are used to display quantitative values over a continuous interval or time period. - **Late (~65%–88%)**: machine learning model on time-series data.; ture of the signal effectively. - **Ending (~88%–100%)**: use this virtual environment with this project.; assumes that our input variables are not highly correlated. 【Key Takeaways】 - **Exposure to Jupyter** (Opening): Exposure to Jupyter, PyCharm, and use of GitHub. - **Objective What is time** (Opening): Objective What is time-series forecasting? - **in fact for using it w…** (Opening): in fact for using it we just need to import it. - **s assigns labels (0 fo…** (Early): s assigns labels (0 for the first element, 1 for the second element, and so on). - **ected to our engine** (Early): ected to our engine, so no need to use the ENGINE parameter. - **Firstly** (Early): Firstly, let’s define the Hypotheses as follows for our example: H0: Average Experience of Current Batch and Previous batch is the same. 【Reading Tips】 - Use Passage locations below to jump into the text and set reading anchors - If this is a brief outline, click Regenerate (top right) for a synthesized guide 【Coverage Limits】 Compressed outline without the model (~19 index chunks). Full structured guide needs AI available.

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Page 12

ollow the link to download the Coloured Images of the book: https://rebrand.ly/75823 Errata We take immense pride in our work at BPB Publications and follow...

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ected to our engine, so no need to use the ENGINE parameter. First I am storing my create table SQL query in a variable named then I am passing this query to...

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ue data. You can access the data from our GitHub repository. As a data scientist, your first task is to read the data in your notebook. Since the data is in...

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ween one dependent binary variable and one or more nominal, ordinal, interval, or ratio-level independent variables. Behind the scenes, the logistic regressi...

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fying an outlier caused by a human error during data entry. Association mining identifies sets of items that frequently occur together in your dataset. Retai...

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ture of the signal effectively. In other cases, it seems to just give a linear fit. This is potentially very useful in the weekly substructure of the signal....

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use this virtual environment with this project. Conclusion In this chapter, you have learned some of the best practices that you will use in any organization...

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ML problems types 321 V validation dataset 322 variance 321 Vector Autoregression Moving-Average (VARMA) 232 W web page traffic, forecasting while loop in Py...

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