donaldchung-hk/inst_0060_datastore

INST0060 - Research Template 1 - Group 12

About

• This project compare 4 models' performance on a bank consumers dataset.
• The four models are:

• Logistic Regression
• Random Forest
• KNN
• Fisher Linear Discriminant

Installation

We provided a separate files with the required libraries to run the experiments: <requirement.txt> The steps to follow to create the relevant environment are in the Requirements section below.

Usage

To run the experiment use the following syntax on your machine's terminal: python model_comparison.py <file.csv> <target_value> <column_to_drop>

The experiment value options are:

• Logistic_Regression ~ ( runtime 1 to 2 min)
• KNN ~ ( runtime 1 min)
• Fisher ~ ( runtime 1 to 2 min)
• Random_Forest ~ ( runtime 1 min)

EXAMPLE: python model_comparison.py Churn_Modelling.csv Exited Random_Forest RowNumber,CustomerId,Surname

Content

The project is structure with:

• model_comparison.py ~ main method which calls methods to fit and evaluate each models
• model_fit folder ~ contains one file for each model. Each file has a main method called in model_comparison.py and the methods used to fit the models
• fomlads.evaluate.eval_classification.py ~ contains methods to evaluate the models
• fomlads.data.preprocessing.py ~ contains method used to pre-process the raw data file
• fomlads.data.external.py ~ contains the methods used to standardise and normalise the data

Requirements

The requirements to run these experiments are contained in the <requirements.txt> file. This file should be used to create an environment following the steps below:

1. conda create -n <name_of_your_environment> python=3.7
2. conda activate <name_of_your_environment>
3. python -m pip install -r requirements.txt

• the requirements.txt file should be in the same directory your currently working on