Intermediate Predictive Analytics in Python

BasicSchwierigkeitsgrad

Aktualisiert 08/2024

Learn how to prepare and organize your data for predictive analytics.

Kursbeschreibung

Building good models only succeeds if you have a decent base table to start with. In this course you will learn how to construct a good base table, create variables and prepare your data for modeling. We finish with advanced topics on the matter.

Voraussetzungen

Introduction to Predictive Analytics in Python

Crucial base table concepts

In this chapter you will learn how to construct the foundations of your base table, namely the population and the target.

The basetable timeline

50 XP

Timeline violations

50 XP

Available data

100 XP

Timeline violation

100 XP

The population

50 XP

Select the relevant population

50 XP

A timeline compliant population

100 XP

Removing duplicate objects

100 XP

The target

50 XP

Calculate an event target

100 XP

Calculate an aggregated target

100 XP

Kapitel starten

Creating variables

You will learn how to add variables to the base table that you can use to predict the target.

Adding fixed variables

50 XP

Selecting the right value

50 XP

Adding age

100 XP

Adding the donor segment

100 XP

Adding living place

100 XP

Adding aggregated variables

50 XP

Selecting the appropriate date

50 XP

Maximum value last year

100 XP

Recency of donations

100 XP

Adding evolutions

50 XP

Ratio of last month's and last year's average

100 XP

Absolute difference between two years

100 XP

Using evolution variables

50 XP

Performance of evolution variables

100 XP

Meaning of evolution

100 XP

Kapitel starten

Data preparation

Once you derived variables from the raw data, it is time to clean the data and prepare it for modeling. In this Chapter we discuss the steps that need to be taken to make your data modeling-ready.

Creating dummies

50 XP

Creating a dummy from a two-category variable

100 XP

Creating dummies from a many-categories variable

100 XP

Missing values

50 XP

How to replace missing values

50 XP

Creating a missing value dummy

100 XP

Replace missing values with the median value

100 XP

Replace missing values with a fixed value

100 XP

Handling outliers

50 XP

Influence of outliers on predictive models

50 XP

Handle outliers with winsorization

100 XP

Handle outliers with standard deviation

100 XP

Transformations

50 XP

Interactions

50 XP

Square root transformation

100 XP

Adding interactions to the basetable

100 XP

Kapitel starten

Advanced base table concepts

In some cases, the target or variables change heavily with the seasons. You will learn how you can deal with seasonality by adding different snapshots to the base table.

Seasonality

50 XP

Seasonality or not

50 XP

Detecting seasonality

100 XP

The effect of seasonality

100 XP

Using multiple snapshots

50 XP

Target values

50 XP

Calculating snapshot targets

100 XP

Calculating aggregated variables

100 XP

Stacking basetables

100 XP

The timegap

50 XP

Events during the timegap

50 XP

Calculating aggregated variables with timegap

100 XP

Adding age with timegap

100 XP

Congratulations

50 XP

Kapitel starten

Intermediate Predictive Analytics in Python

Kurs
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Intermediate Predictive Analytics in Python

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Kursbeschreibung

Voraussetzungen

Crucial base table concepts

Creating variables

Data preparation

Advanced base table concepts

Leistungsnachweis verdienen

Schließe dich 19 Millionen Lernenden an und starte Intermediate Predictive Analytics in Python heute!

DataCamp gibt es auch für Mobilgeräte

Kursbeschreibung

Leistungsnachweis verdienen

Schließe dich .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 Millionen Lernenden an und starte Intermediate Predictive Analytics in Python heute!

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DataCamp gibt es auch für Mobilgeräte

Schließe dich 19 Millionen Lernenden an und starte Intermediate Predictive Analytics in Python heute!