Machine Learning Algorithms using Java

About

A fun side program to perform machine learning algorithm using plain java code.

The Naive Bayes code is dynamic for different datasets as long as all columns in the dataset are used and the last column is the result.

Note: For datasets not involving the first or first few columns such as the 3rd dataset example, change the iterations as commented in the code.

Technology Stack

1. Developed using plain Java

Algorithms

1. Naive Bayes (using dynamic datasets)
2. Linear Regression (using static dataset)
3. K-Medoids Clustering (using static dataset)

Developer

• Adnan Hakim github.com/adnanhakim

Build

• Change the dataset as required

Datasets (Naive Bayes)

Dataset 1 - To predict if certain weather occurs

Example Set - < rain, hot, humid, false > (N)

outlook	temperature	humidity	windy	class
sunny	hot	high	false	N
sunny	hot	high	true	N
overcast	hot	high	false	P
rain	mild	high	false	P
rain	cool	normal	false	P
rain	cool	normal	true	N
overcast	cool	normal	true	P
sunny	mild	high	false	N
sunny	cool	normal	false	P
rain	mild	normal	false	P
sunny	mild	normal	true	P
overcast	mild	high	true	P
overcast	hot	normal	false	P
rain	mild	high	true	N

Dataset 2 - To predict if a car with certain properties will be stolen or not

Example Set - < red, suv, domestic > (no)

color	type	origin	stolen
red	sports	domestic	yes
red	sports	domestic	no
red	sports	domestic	yes
yellow	sports	domestic	no
yellow	sports	imported	yes
yellow	suv	imported	no
yellow	suv	imported	yes
yellow	suv	domestic	no
red	suv	imported	no
red	sports	imported	yes

Dataset 3 - To predict if a person under certain conditions will be suburned or not

Example Set - < brown, tall, average, no > (none)

name	hair	height	weight	dublin	result
Sarah	blonde	average	light	no	sunburned
Dana	blonde	tall	average	yes	none
Alex	brown	short	average	yes	none
Annie	blonde	short	average	no	sunburned
Emily	red	average	heavy	no	sunburned
Pete	brown	tall	heavy	no	none
John	brown	average	heavy	no	none
Katie	brown	short	light	yes	none

Dataset 4 - To predict if a person with certain properties will buy a computer or not

Example Set - < <=30, medium, yes, fair > (yes)

age	income	student	credit_rating	buys_computer
<=30	high	no	fair	no
<=30	high	no	excellent	no
31...40	high	no	fair	yes
>40	medium	no	fair	yes
>40	low	yes	fair	yes
>40	low	yes	excellent	no
31...40	low	yes	excellent	yes
<=30	medium	no	fair	no
<=30	low	yes	fair	yes
>40	medium	yes	fair	yes
<=30	medium	yes	excellent	yes
31...40	medium	no	excellent	yes
31...40	high	yes	fair	yes
>40	medium	no	excellent	no

MIT LICENSE

Copyright (c) 2020 Adnan Hakim

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.