Feature Engineering
In order to predict the outcome variable (y variable) you need to turn all the object type columns into numeric in order to predict the Sale Price. So first, I converted all the ordinal columns into numeric by assigning them by numbers, and for all the other features I created dummy variables.
17
177 reads
CURATED FROM
IDEAS CURATED BY
A machine learning example
“
The idea is part of this collection:
Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Related collections
Similar ideas to Feature Engineering
Types Of Data
- Nominal: used for labelling variables(m- male and f- female)
- Ordinal: used for measuring non-numeric with an order of the values(1-unhappy, 2-ok, 3- happy)
- Data Cleaning: In this data set, there are 2051 rows with 80 colum...
Feature extraction and suitable machine learning model
When dealing with large datasets with many columns and variables, feature extracting is used to divide and reduce existing data into a manageable group.
But for image processing, machines can't extract features such as edges, shapes, or even size in this way
Transfer learning and fine tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
- Take layers from...
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Personalized microlearning
—
100+ Learning Journeys
—
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates