A Practical Guide to Machine Learning with Python/

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MD Implementation Steps: 3 to 8

This lesson will continue to introduce the implementation steps (3-8) of model design.

We'll cover the following...

3) Exploratory data analysis
4) Data scrubbing
5) Pre-model algorithm (optional)
6) Split validation
7) Set algorithm
8) Predict

3) Exploratory data analysis

The third step, EDAExploratory Data Analysis, provides an opportunity to become familiar with your data including distribution and the state of missing values. Exploratory data analysis also drives the next stage of data scrubbing and your choice of algorithm.

In addition, EDAExploratory Data Analysis may come into play in other sections of your code as you check the size and structure of your dataset and integrate that feedback to direct model optimization.

4) Data scrubbing

The data scrubbing stage usually consumes the ...

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

MD Implementation Steps: 3 to 8

3) Exploratory data analysis

4) Data scrubbing