Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

MLtarball.tar.gz

jupyter1

jupyter2

jupyter3

jupyter4

jupyter5

jupyter6

jupyter7

jupyter8

jupyter9

jupyter10

jupyter11

jupyter12

jupyter13

jupyter14

jupyter15

jupyter16

jupyter17

jupyter18dtextra

jupyter19dt

jupyter20rf

jupyter21gbc

jupyter22gbr

jupyter-temp

pca6

split

lor1

lor2

lor3

lor4

lor5

lor6

lor7

lor8

lor9

svm9

knn9

gbc7

gbr1

gbr2

gbr3

gbr4

gbr5

gbr6

gbr7

gbr8

Mlxtend

This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# 4) Remove or modify variables with missing values
Our exploratory data analysis shows that missing values pose a problem for this dataset, especially since a linear regression does not run smoothly with missing values. Therefore, we need to estimate or remove these values from the data frame.

However, the data frame size will be greatly reduced if you choose to remove all missing values on a row-by-row basis. The variable `BuildingArea,` for instance, has 21,115 missing rows, which makes up two-thirds of the data frame! 
To preserve row depth, you can remove this variable entirely, especially as it’s not highly correlated with the dependent variable of `Price` (0.1).

The remaining variables can be removed on a row-by-row basis or filled with the mean value. Based on exploratory data analysis, you can:

- Use the mean to fill variables with partial correlation to `Price` (i.e., `Car`).
- Remove rows for variables with a small number of missing values (i.e., `Distance`).
- Avoid filling values for variables with significant correlation to `Price` and, instead, remove those missing values row-by-row (i.e., `Bathroom`). 

We will continue to go through the implementation steps (4-8) of linear regression.

LR Implementation Steps: 4 to 7

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

LR Implementation Steps: 4 to 7

4) Remove or modify variables with missing values