MD Implementation Steps: 3 to 8

Explore essential steps in designing and implementing machine learning models including exploratory data analysis, thorough data cleaning, algorithm selection, data splitting for validation, and using prediction functions to validate your model with test data.

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 ...

1.Introduction to Course

2.Introduction to Machine Learning

3.Exploratory Data Analysis

4.Data Scrubbing

5.Pre-Model Algorithms

Project

6.Split Validation

7.Model Design

8.Linear Regression

9.Logistic Regression

10.Support Vector Machines

11.K-Nearest Neighbors

12.Tree-Based Methods

Project

13.Conclusion

14.Appendix

MD Implementation Steps: 3 to 8

3) Exploratory data analysis

4) Data scrubbing