Intermediate Predictive Analytics in Python

基础技能水平

更新时间 2024年8月

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

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

课程描述

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.

先决条件

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 经验值

Timeline violations

50 经验值

Available data

100 经验值

Timeline violation

100 经验值

The population

50 经验值

Select the relevant population

50 经验值

A timeline compliant population

100 经验值

Removing duplicate objects

100 经验值

The target

50 经验值

Calculate an event target

100 经验值

Calculate an aggregated target

100 经验值

开始章节

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 经验值

Selecting the right value

50 经验值

Adding age

100 经验值

Adding the donor segment

100 经验值

Adding living place

100 经验值

Adding aggregated variables

50 经验值

Selecting the appropriate date

50 经验值

Maximum value last year

100 经验值

Recency of donations

100 经验值

Adding evolutions

50 经验值

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

100 经验值

Absolute difference between two years

100 经验值

Using evolution variables

50 经验值

Performance of evolution variables

100 经验值

Meaning of evolution

100 经验值

开始章节

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 经验值

Creating a dummy from a two-category variable

100 经验值

Creating dummies from a many-categories variable

100 经验值

Missing values

50 经验值

How to replace missing values

50 经验值

Creating a missing value dummy

100 经验值

Replace missing values with the median value

100 经验值

Replace missing values with a fixed value

100 经验值

Handling outliers

50 经验值

Influence of outliers on predictive models

50 经验值

Handle outliers with winsorization

100 经验值

Handle outliers with standard deviation

100 经验值

Transformations

50 经验值

Interactions

50 经验值

Square root transformation

100 经验值

Adding interactions to the basetable

100 经验值

开始章节

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 经验值

Seasonality or not

50 经验值

Detecting seasonality

100 经验值

The effect of seasonality

100 经验值

Using multiple snapshots

50 经验值

Target values

50 经验值

Calculating snapshot targets

100 经验值

Calculating aggregated variables

100 经验值

Stacking basetables

100 经验值

The timegap

50 经验值

Events during the timegap

50 经验值

Calculating aggregated variables with timegap

100 经验值

Adding age with timegap

100 经验值

Congratulations

50 经验值

开始章节

Intermediate Predictive Analytics in Python

课程完成

获得成就证明

将此证书添加到你的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Intermediate Predictive Analytics in Python！

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

Intermediate Predictive Analytics in Python

创建您的免费帐户

培训2人或更多？

课程描述

先决条件

Crucial base table concepts

Creating variables

Data preparation

Advanced base table concepts

获得成就证明

加入超过19百万学习者，今天就开始Intermediate Predictive Analytics in Python！

创建您的免费帐户

通过 DataCamp for Mobile 提升您的数据技能

课程描述

获得成就证明

加入超过.css-nklxlk{color:var(--wf-brand--main, #03EF62);}19百万学习者，今天就开始Intermediate Predictive Analytics in Python！

创建您的免费帐户

通过 DataCamp for Mobile 提升您的数据技能

加入超过19百万学习者，今天就开始Intermediate Predictive Analytics in Python！