Data Science Projects Using Python Step - by - step guide for data science beginners with a pract...

Data Science Projects Using Python Steby - step guide for data science beginners with a practical approach | 12.51 MB

Title: Data Science Projects Using Python
Author: Dr. Pratiyush Guleria
Category: Nonfiction, Computers, Database Management, Data Processing, Advanced Computing, Artificial Intelligence, Programming, Programming Languages
Language: English | 318 Pages | ISBN: 9365894549

Description:
Gain 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.

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