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

Linimasa base table

Pelanggaran lini masa

Data yang tersedia

Pelanggaran linimasa

Populasi

Pilih populasi yang relevan

Populasi yang sesuai linimasa

Menghapus objek duplikat

Target

Hitung target acara

Hitung target teragregasi

Crucial base table concepts

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

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

Creating variables

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

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

Data preparation

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

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

Advanced base table concepts

Donor IDs

Basetable with countries and age

Basetable used in Ex 2.13

Living place of donors

Donations

Membangun model yang baik hanya berhasil jika Anda memiliki base table yang layak sebagai awal. Dalam kursus ini Anda akan mempelajari cara membangun base table yang baik, membuat variabel, dan menyiapkan data untuk pemodelan. Kita akhiri dengan topik lanjutan terkait hal tersebut.

Introduction to Predictive Analytics in Python

Pelajari cara menyiapkan dan mengatur data untuk analitik prediktif dengan Python.

Predictive Analytics Tingkat Menengah dengan Python

Pelajari cara mempersiapkan dan mengorganisir data Anda untuk analisis prediktif.

Pilih populasi yang relevan

Predictive Analytics Tingkat Menengah dengan Python

Latihan interaktif praktis