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CORSO PYTHON Evoluto
Durata: 3 gg (20 ore)
CASE STUDY: CREDIT RISK EXPLORATORY ANALYSIS, con algoritmi di Machine Learning di
classificazione, e algoritmi di regressione lineare per imputazione ed estrapolazione
Sfruttando il Case Study verrano ripresi i concetti
Introduction - Data
○ Pandas and Numpy
○ Importing data
○ Data description and evaluation
○ Data Visualization with Matplotlib and Seaborn
Feature Engineering
● Data preparation
○ Feature engineering
○ Imputation: filling missing data; Normalization
○ Managing outliers, cap and floor
○ Some Example with Kaggle Models, Titanic and MPG
Data Viz Techniques and Reporting
● Distribuzioni e statistiche di summary
○ summary statistics for categorical variables,
○ visualization for distribution categorical data,
○ summary statistics for numerical variables,
○ distribution visualization for numerical variables
○ summary statistics for correlation
● visualize correlation
● time-related patterns in data
● find structural breaks in data
○ time series analysis with prophet and techniques to replace auto-regression with tree-
based analysis
○ Static and interactive reporting