Machine Learning - Project 1

'NeMozeNamNikoNista' team repository used for managing and submitting Project 1 of the EPFL's Machine Learning course (CS-433).

Team members:

• Dino Mujkić (dinomujki)
• Hrvoje Bušić (hrvojebusic)
• Sebastijan Stevanović (sebastijan94)

Getting started

Prerequisites

• Python 3 - installation and instructions
• NumPy - installation and instructions

Data

To get started you must obtain the data used for the EPFL's ML Course - Project 1 competition available on Kaggle.

Following files are required:

• train.csv
• test.csv

Data needs to be placed in the existing ./data subdirectory.

Results

Running run.py from the ./regression subdirectory will generate submission file submission.csv in the ./data subdirectory.

Content

Data wrangling

Files located in the ./data_wrangling subdirectory contain functions used for initial data loading and manipulation, as well as the creation of the final submission files. The methods applied in the initial data manipulation phase are explained in the project's report.

Regression

Files connected to regression implementation can be found in the ./regression subdirectory.

• cross_validation.py - functions used for cross validation testing
• implementations.py - the file contains 6 necessary algorithm implementations for this project as well as appropriate helper functions
• polynomial.py - a function used to construct polynomial bases
• run.py - a main execution file which performs data parsing and model construction to generate the optimal submission results
• test_run_implementations.py - function used to test the implementations using a dummy dataset

Neural network

Files connected to neural network implementation can be found in the ./neural_network subdirectory.

• data_loader.py - utility functions for working with .csv files when loading test and validation sets, and storing results
• genetic_algorithm.py - implementation of generational genetic algorithm
• network_layers.py - abstraction of network layer used by a neural network with several implementations
• neural_net.py - a high level object that consists of network layers, and can for a given vector of input instances return the vector of outputs for all the input instances
• runner.py - execution file which generates predictions using neural network model
• transfer_functions.py - transfer functions that can be used on the neuron's output

Code reuse

We encourage you to fork or clone this repository for future development. Issue reporting and pull requests are welcome as well.