Interactive Course

Intermediate Predictive Analytics in Python

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

4 hours
15 Videos
56 Exercises
2,219 Participants
4,350 XP

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Course Description

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.

Intermediate Predictive Analytics in Python

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

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.

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

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.

1
Crucial base table concepts
Free

In this chapter you will learn how to construct the foundations of your base table, namely the population and the target.
View Chapter Details Play Chapter Now
Creating variables

You will learn how to add variables to the base table that you can use to predict the target.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

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Nele Verbiest

Data Scientist at Python Predictions

Nele is a senior data scientist at Python Predictions, after joining in 2014. She holds a master’s degree in mathematical computer science and a PhD in computer science, both from Ghent University. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities. Nele has a keen interest in big data technologies and business applications

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Intermediate Predictive Analytics in Python

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Loved by learners at thousands of top companies:

Course Description

Crucial base table concepts

The basetable timeline

Timeline violations

Available data

Timeline violation

The population

Select the relevant population

A timeline compliant population

Removing duplicate objects

The target

Calculate an event target

Calculate an aggregated target

Data preparation

Creating dummies

Creating a dummy from a two-category variable

Creating dummies from a many-categories variable

Missing values

How to replace missing values

Creating a missing value dummy

Replace missing values with the median value

Replace missing values with a fixed value

Handling outliers

Influence of outliers on predictive models

Handle outliers with winsorization

Handle outliers with standard deviation

Transformations

Interactions

Square root transformation

Adding interactions to the basetable

Creating variables

Adding fixed variables

Selecting the right value

Adding age

Adding the donor segment

Adding living place

Adding aggregated variables

Selecting the appropriate date

Maximum value last year

Recency of donations

Adding evolutions

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

Absolute difference between two years

Using evolution variables

Performance of evolution variables

Meaning of evolution

Advanced base table concepts

Seasonality

Seasonality or not

Detecting seasonality

The effect of seasonality

Using multiple snapshots

Target values

Calculating snapshot targets

Calculating aggregated variables

Stacking basetables

The timegap

Events during the timegap

Calculating aggregated variables with timegap

Adding age with timegap

Congratulations