ML Models Overview

This project is a practical knowledge recopilation for testing different ML models based on the same problematic. All models from the same category are prepared and trained with a common dataset.

Please refer to the following table for check all the files content:

ID	Name	Type	Description
1	magic04	.data	DataSet for Magic Gamma Telescope practice
2	SeoulBikeData	.csv	DataSet for Seoul Bikes Demand practice
3	Supervised CM	.ipynb	Supervised Classification Models practice and overview
4	Supervised RM	.ipynb	Supervised Regression Models practice and overview

Magic Gamma Telescope - SCM

This practice aims to predict wheter a ray caught from the Atmospheric Cherenkov Telescope is Gamma or Hadron classification. In the practice the following ML models were tested in order to find the most suitable solution:

• K-Nearest Neighbors
• Naive Bayes
• Log Regression
• Support Vector Machines
• Neural Network

Attribute Information

The following table contains the summarized information about the dataset attributes used for this ML model. This attributes are also known as Features that will be passed into this model in order to predict a label, which in this case is the class column (Ray Classification).

ID	Name	Type	Description
1	fLength	continuous	major axis of ellipse [mm]
2	fWidth	continuous	minor axis of ellipse [mm]
3	fSize	continuous	10-log of sum of content of all pixels [in #phot]
4	fConc	continuous	ratio of sum of two highest pixels over fSize [ratio]
5	fConc1	continuous	ratio of highest pixel over fSize [ratio]
6	fAsym	continuous	distance from highest pixel to center, projected onto major axis [mm]
7	fM3Long	continuous	3rd root of third moment along major axis [mm]
8	fM3Trans	continuous	3rd root of third moment along minor axis [mm]
9	fAlpha	continuous	angle of major axis with vector to origin [deg]
10	fDist	continuous	distance from origin to center of ellipse [mm]
11	class	g,h	gamma (signal), hadron (background)

Seoul Bikes - SRM

This practice aims to predict the number of public bicycles rented at noon in the Seoul Bike Sharing System. In the practice the following ML models were tested in order to find the most suitable solution:

• Linear Regression
• Multiple Linear Regression
• Regression with Neural Network
• Neural Network

Attribute Information

The following table contains the summarized information about the dataset attributes used for this ML model. This attributes are also known as Features that will be passed into this model in order to predict a quantity, which in this case is the bike_count column.

ID	Name	Type	Description
1	bike_count	discrete	count of bikes rented at each hour
2	hour	discrete	hour of the day
3	temp	continuous	temperature [celsius]
4	humidity	discrete	humidity [ratio]
5	wind	continuous	windspeed [m/s]
6	visibility	discrete	10m
7	dew_pt_temp	continuous	[celsius]
8	radiation	continuous	[MJ/m2]
9	rain	continuous	[mm]
10	snow	continuous	[cm]
11	functional	nominal	functional day