Use pandas and MatDescriptionlib to critically examine a dataset with summary statistics and graphs and extract meaningful insights.

Learn

Install the required packages to set up a data science coding environment
Load data into a Jupyter Notebook running Python
Use MatDescriptionlib to create data visualizations
Fit a model using scikit-learn
Use lasso and ridge regression to reduce overfitting
Fit and tune a random forest model and compare performance with logistic regression
Create visuals using the output of the Jupyter Notebook

About

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and MatDescriptionlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you'll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.

Features

Practical guidance on industry-standard data analysis and machine learning tools
Tune algorithms to provide the best predictions on new unseen data
Gain knowledge on how to prepare data and feed it to machine learning algorithms
Gain insight into the predictive capabilities of the models and the way they make predictions

Citación :

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