Courses

Master Python Data Analysis and Modelling Essentials

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In this real-world project, we used Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, and many other tools.

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Data is analyzed and modelled in this process

Setting up a Python environment for data analysis and model building

Rename the data fields

The process of slicing, sorting, filtering, and grouping data

Find out if a value is missing and put it in

Detection and treatment of outliers

Correlation analysis and the selection of features

Data is split up for fitting and testing models

Data normalization in a variety of ways

In statistics, this means making a classic linear regression model

An example: Making a machine linear regression model

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