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

# 1) Import libraries
Let’s begin by importing the following Python libraries:




#1. Import libraries
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics

> __Note__: Codes of further steps won't include codes of previous steps. They're already appended at the backend for you.

# 2) Import dataset
Using the Pandas `pd.read_csv` command, load the CSV dataset into a data frame and assign the data frame as a variable called df using the equals operator.

Melbourne housing dataset variables

align-center

Please note that the Latitude and Longitude variables are misspelled in this dataset, but this won’t affect our code, as you’ll remove these two variables in Step 3.

#2. Import dataset
df = pd.read_csv('Melbourne_housing_FULL.csv')

# 3) Remove variables 
Regression models can be developed in two ways. The first is to follow the principle of parsimony, which uses a limited or small number of variables to explain a large proportion of the variance. The other is to capture maximum variance, which uses more variables, including variables that explain a small proportion of variance.

There are pros and cons to both methods, with the addition to other considerations such as computational resources and model complexity.

For our model, we will focus on using a limited number of variables for reasons of convenience. However, we will also build our model using variables already expressed numerically, including `Price`, `Distance`, `BuildingArea`, `Bedroom2`, `Bathroom`, `Rooms`, `Car`, `Propertycount`, and `Landsize`. This means you can ignore and remove non-numerical variables such as `Address`, `Method`, `SellerG`, `Date`, etc., which would otherwise need to be transformed into a numerical format for the algorithm to read them.

This lesson will start introducing the implementation steps (1-3) of the linear regression.

LR Implementation Steps: 1 to 3

Feature	Data Type	Continuous/Discrete
Suburb	String	Discrete
Address	String	Discrete
Rooms	Integer	Continuous
Type	String	Discrete
Price	Integer	Continuous
Method	String	Discrete
SellerG (seller's name)	String	Discrete
Date	TimeDate	Discrete
Distance	Floating-point	Continuous
Postcode	Integer	Discrete
Bedroom2	Integer	Continuous
Bathroom	Integer	Continuous
Car	Integer	Continuous
Landsize	Integer	Continuous
BuildingArea	Integer	Continuous
YearBuilt	TimeDate	Discrete
CouncilArea	String	Discrete
Latitude	String	Discrete
Longitude	String	Discrete
Regionname	String	Discrete
Propertycount (is that suburb)	Integer	Continuous

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: 1 to 3

1) Import libraries

2) Import dataset

Melbourne housing dataset variables