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Intermediate Predictive Analytics in Python

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

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4 Hours15 Videos56 Exercises3,976 Learners4350 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.

  1. 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.

    Play Chapter Now
    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
  2. 2

    Creating variables

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

    Play Chapter Now
    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
  3. 3

    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.

    Play Chapter Now
    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
  4. 4

    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.

    Play Chapter Now
    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

Datasets

Donor IDsBasetable with countries and ageBasetable used in Ex 2.13Living place of donorsDonations

Collaborators

loreLore DiricknicksolomonNick Solomonhadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroix

Prerequisites

Introduction to Predictive Analytics in Python
Nele Verbiest Headshot

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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